• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.3
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• pp.51-64
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• 2015

Climate change will make significant impact on species distribution in forest. Pinus koraiensis which is commonly called as Korean Pine is normally distributed in frigid zones. Climate change which causes severe heat could affect distribution of Korean pine. Therefore, this study predicted the distribution of Korean Pine and the suitable habitat area with consideration on uncertainty by applying climate change scenarios on an ensemble model. First of all, a site index was considered when selecting present and absent points and a stratified method was used to select the points. Secondly, environmental and climate variables were chosen by literature review and then confirmed with experts. Those variables were used as input data of BIOMOD2. Thirdly, the present distribution model was made. The result was validated with ROC. Lastly, RCP scenarios were applied on the models to create the future distribution model. As a results, each individual model shows quite big differences in the results but generally most models and ensemble models estimated that the suitable habitat area would be decreased in midterm future(40s) as well as long term future(90s).

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.3
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• pp.29-43
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• 2024

Legally protected species are one of the crucial considerations in the field of natural ecology when conducting environmental impact assessments (EIAs). The occurrence of legally protected species, especially 'Endangered Wildlife' designated by Ministry of Environment, significantly influences the progression of projects subject to EIA, necessitating clear investigations and presentations of their habitats. In perspective of statistics, a minimum of 30 occurrence coordinates is required for population prediction, but most of endangered wildlife has insufficient coordinates and it posing challenges for distribution prediction through modeling. Consequently, this study aims to propose modeling methodologies applicable when coordinate data are limited, focusing on Rodgersia podophylla, representing characteristics of endangered wildlife and northern plant species. For this methodology, 30 random sampling coordinates were used as input data, assuming little survey data, and modeling was performed using individual models included in BIOMOD2. After that, the modeling results were evaluated by using discrimination capacity and the reality reflection ability. An optimal modeling technique was proposed by ensemble the remaining models except for the MaxEnt model, which was found to be less reliable in the modeling results. Alongside discussions on discrimination capacity metrics(e.g. TSS and AUC) presented in modeling results, this study provides insights and suggestions for improvement, but it has limitations that it is difficult to use universally because it is not a study conducted on various species. By supporting survey site selection in EIA processes, this research is anticipated to contribute to minimizing situations where protected species are overlooked in survey results.

• Korean Journal of Environment and Ecology
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• v.36 no.3
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• pp.303-317
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• 2022

Climate change caused by increased greenhouse gas emissions can alter the natural ecosystem, including the pollination ecosystem and agricultural ecology, which are ecological interactions between potted insects and plants. Many studies have reported that populations of wild bees, including bees and wasps (BW), which are the key pollinators, have gradually declined due to climate change, leading to adverse impacts on overall biodiversity, ultimately with agribusinesses and the life cycle of flowering plants. Therefore, we could infer that the rising temperature in Korean Peninsula (South Korea) due to global warming has led to climate change and influenced the wild bee's ecosystem. In this study, we surveyed the distributional pattern of BW (Superfamily: Apoidea, Vespoidea, and Chrysidoidea) at 51 sites from 2017 (37 sites) to 2018 (14 sites) to examine the effects of climatic factors on the nationwide distribution of BW in South Korea. Previous literature has confirmed that their distribution according to forest climate zones is significantly correlated with mean and accumulative temperatures. Based on the result, we predicted the effects of future climate changes on the BW distribution that appeared throughout South Korea and the species that appeared in specific climate zones using Shared Socioeconomic Pathways (SSPs). The distributions of wild BW predicted by the SSP scenarios 2-4.5 and 5-8.5 according to the BIOMOD species distribution model revealed that common and endemic species will shift northward from the current habitat distribution by 2050 and 2100, respectively. Our study implies that climate change and its detrimental effect on the ecosystem is ongoing as the BW distribution in South Korea can change, causing the change in the ecosystem in the Korean Peninsula. Therefore, immediate efforts to mitigate greenhouse gas emissions are warranted. We hope the findings of this study can inspire further research on the effects of climate change on pollination services and serve as the reference for making agricultural policy and BW conservation strategy

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.47-52
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• 2014

Species distribution models have been widely applied in order to assess biodiversity, design reserve, manage habitat and predict climate change. However, SDMs has been used restrictively to the public and policy sectors owing to model uncertainty. Recent studies on ensemble and consensus models have been increased to reduce model uncertainty. This paper was carried out single model and multi model for Corylopsis coreana and compares two models. First, model evaluation was used AUC, kappa and TSS. TSS was the most effective method because it was easy to compare several models and convert binary maps. Second, both single and ensemble model show good performance and RF, Maxent and GBM was evaluated higher, GAM and SRE was evaluated lower relatively. Third, ensemble model tended to overestimate over single model. This problem can be solved by the suitable model selection and weighting through collaboration between field experts and modeler. Finally, we should identify causes and magnitude of model uncertainty and improve data quality and model methods in order to apply special decision-making support system and conservation planning, and when we make policy decisions using SDMs, we should recognize uncertainty and risk.