Biodiversity is rapidly declining globally and targeted efforts are needed to mitigate the loss of species. Conventional conservation efforts have focused on establishing protected areas and restoring degraded lands in order to maintain current conditions or restore ecosystems to a pre-damaged state. However, as the climate changes, the current bioclimatic zones will be re-distributed globally. Historical distribution patterns may no longer serve as an effective guide for supporting biodiversity under climate change. In response to these challenges, this study proposes a spatially explicit strategy for biodiversity conservation that takes climate change into account using bioclimatic classification. The bioclimatic classification maps of Northeast Asia (NEA) were constructed for three historical time periods (the 1980s, 1990s, and 2000s) and two future time periods (the 2050s and 2070s) using five general circulation models (GCMs) in representative concentration pathway (RCP 8.5) scenarios. It was predicted that, in general, zones are shifting north, and some zones are expanding or shrinking rapidly. Based on an analysis of latitudinal and areal change for each zone, the bioclimate vulnerability index (BVI) and naturality index (NI) were developed to quantify the impact of environmental change. As a result of the BVI analysis, the distribution of vulnerable zones is expected to shift northward and expand. As is evident with the increased vulnerability of the subarctic region caused by the expansion of the temperate climate, the extent of vulnerable zones will increase. Also, the southern regions of NEA are becoming vulnerable due to the transformation of the temperate zone to a more subtropical zone. Quadrant graphs based on the BVI and NI were created to present appropriate strategies for each zone. Our proposed framework shows that conservation strategies should be modified based on the changes in the relative position of each zone over time.

According to the guidelines of the Nagoya Protocol, species are now recognized as ‘resources’ and owned by each country, thereby emphasizing the significance of biological resources and the importance of the continuous efforts made to systematically manage them. Despite these efforts, climate change, which influences climatic factors such as temperature and precipitation, is expected to negatively impact the struggle for conservation of biological resources by affecting species’ habitats. We aimed to devise methodologies that could be utilized for the management of biological resources, especially valuable tree species, that are experiencing difficulties due to climate change. First, changes in habitat of the northern-region plant Needle fir (Abies holophylla) due to of climate change were estimated using the BIOMOD2 package in R under the RCP8.5 scenario. Second, the time period of management was estimated based on the change in habitat area over time. It is expected that 30% of the current habitat of A. holophylla will be lost by 2030 and 50% will be lost by 2042. Third, four management zones (maintenance, reduction, dispersal, and non-habitat areas) were derived by comparing habitats according to the period of management required. In this case, we compared the present and the time point at which 30% habitat loss (2030) is expected to occur. After that, the management steps that can be taken for each management zone were suggested. Our results show the impact of climate change, especially change in Bio1 (annual mean temperature) and Bio13 (precipitation of wettest month), on species distribution patterns and have potential applicability in biological resource management. We have specified the suitable point of time, area, and direction of management in this study, which will contribute to climate change management planning and policy-making. By doing so, we hope that when a management policy on biological resources is applied, by dividing the four management zones, policymakers will be able to apply a cost-efficient policy.

Species distribution models (SDMs) are widely used for biodiversity assessment, habitat management, and climate change impact assessment due to their ability to quantitatively evaluate species distribution. However, due to model uncertainty, the use of SDMs in public policy management has been limited. In order to overcome the limitations, many studies have been conducted mainly focusing on an ensemble approach, which compensates for the uncertainty of a single model. Even though ensemble methodology has been proven to improve accuracy compared to single models, this was based on inner validation. As inner validation has established flaws, with using the data in the form of ‘point’, the need to assess outer validation with independent data in a polygon formations has been raised. In this study, we evaluated the accuracy of a Committee Averaging (CV) ensemble methodology using outer validation. In order to minimize uncertainty beyond the methodology setting, we used Pinus thunbergii, which has spatial specificity. As the outer validation method showed more accurate evaluation results, we used outer validation indices-sensitivity, specificity and accuracy-for comparison analysis between ensemble and single model results. Single models tend to overestimate compared to ensemble models, with a high value of sensitivity and a low value of specificity, whereas ensemble models tended to decrease the spatial uncertainty of single models, with generally high values of sensitivity, specificity and accuracy. Accordingly, the ensemble model methodology proved to improve accuracy by reducing the uncertainty of single models. Furthermore, through comparison analysis between outer and inner validation results, we additionally interpreted differences and limitations among inner validation, and have finally confirmed the need for further consideration in interpreting the results of the inner validation for both methodologies. Hence, outer validation using independent data should also be used.

It is essential to maintain the health of forests so that they are protected against a diverse range of stressors and show improved resilience. An area-based forest health map is required for efficient forest management on a national scale however, most national forest inventories are based on in-situ observations. This study examined methodologies to establish an area-based map on tree vitality grade using field survey data, particularly that containing information on several trees at one point. The forest health monitoring dataset of the Republic of Korea was used in combination with 37 satellite-based environmental predictors. Four methods were considered: Multinomial logistic regression (MLR), random forest classification (RF), indicator kriging (IK), and multi-model ensemble (MME) approaches using species distribution models. The MLR and RF produced biased results, whereby almost all regions were classified as first grade; the spatialization results of these methods were considered inappropriate for forest management. The maps produced using the IK and MME methods improved the distinctions between the distributions of five grades compared to the previous two methodologies however, the MME method produced better results, reliably reflecting topographical and climatic characteristics. Comparisons with the vegetation condition index and bioclimate vulnerability index also emphasized the usefulness of the MME. This study is particularly relevant to the national forest managers who struggle to find the most effective forest monitoring and management strategies. Suggestions to improve spatialization of field survey data are further discussed.

As biodiversity continues to decline under the combined pressures of climate change and urbanization, there is an urgent need for comprehensive risk assessment. Given the close interconnection between biodiversity and ecosystem functions, two major risk factors have been identified: (1) habitat loss (destruction) and (2) habitat gain expansion (disturbance). To address these challenges, this study proposes an integrated approach for biodiversity risk assessment that spatially and quantitatively evaluates risks from a holistic perspective. Specifically, an integrative modeling framework (BIOMOD2–LCM–MigClim) was developed to predict biodiversity patterns under the simultaneous impacts of climate change and urbanization. In parallel, novel indices, the Persistence Index (PI) and Disturbance Index (DI), were designed to assess biodiversity risks from multidimensional temporal and spatial perspectives. Based on the biodiversity risk types defined as Risk 1 (habitat loss; climate change risk species) and Risk 2 (habitat gain–expansion; invasive species), the study derived spatial distribution patterns and conducted a time series analysis of risk characteristics. Results highlight the dynamic ecological interactions in competitive areas, where Risk 2 advances by intruding into Risk 1 zones, thereby accelerating Risk 1 retraction. These findings confirm the need for integrated methodologies to capture shifting biodiversity responses and provide robust decision support for fundamental biodiversity policies.