Environmental crisis challenges the human race harder than ever before. Ecologists have produced a massive amount of data to cope with the crisis. Accordingly, many national scale ecological database systems have been developed worldwide to manage and analyse these datasets. However, in Korea, ecological datasets produced by different research institutes for different purposes have not been integrated or serviced due to poorly designed information infrastructure. To address this obstacle, we present EcoBank (www.nie-ecobank.kr), an open, web-based ecological database platform designed to play an important role in ecosystem analysis, not only in Korea, but also worldwide. The architecture of EcoBank comprises core technologies of WebGIS, Application Programming Interface (API), responsive web and open-source software (OSS). Comprehensive ecological datasets from three different sources, including the National Institute of Ecology (NIE) in Korea, related national and international platforms and repositories, enter the three conceptual modules in EcoBank: data management, analysis and service. Diverse potential stakeholders of EcoBank can be classified into three groups: researchers, policy-makers and public users. EcoBank aims to expand its horizons through mutual communication amongst these stakeholders. We opened and launched the EcoBank service in December 2019 and have now begun to broaden its network by linking it to other data platforms and repositories over the globe to find possible solutions to ecological issues in Korea.

The rapidly changing climate is impacting species globally at an unprecedented rate, including humans. Consequently, extensive research is being conducted on the impacts of climate change on indigenous and vulnerable species. However, landscape trees, which are cultivated and managed by humans, receive less attention despite their significant role in urban environments. Landscape tree also have specific climatic ranges and environmental requirements, making them susceptible to climate change. In this study, we predicted the future sustainability of three native landscape trees (Stewartia koreana, Betula ermanii, and Taxus cuspidata) using maximum entropy (MaxEnt) models under SSP2-4.5 and SSP5-8.5 climate scenarios. A time-series analysis of suitability was conducted, and the resulting maps were overlaid to classify regions of suitability. The findings indicate a general northward shift in climate suitability and a potential reduction in long-term suitable areas for all three species. Under the SSP2-4.5 scenario, potential suitable area for S. koreana increased, while those for B. ermanii, T. cuspidata decreased by the 2090s. Under the SSP5-8.5 scenario, suitable areas for S. koreana, B. ermanii, T. cuspidata decreased by 33.6%, 98.9%, and 90.1%, respectively. The climate suitability classification (“Sustainable suitability”, “Risk”, “Inflow”, “Lost”, and “Variable” regions) effectively identified areas of sustainability and risk, as well as regions requiring management. A notable decline in “Sustainable suitability” regions, which remained suitable from the present to the 2090s, was observed under the SSP5-8.5 scenario relative to SSP2-4.5. The methods utilized in this study offer valuable insights for future landscape planning and conservation. This research underscores the need for adaptive strategies to mitigate potential economic and ecological impacts of climate change by utilizing species distribution models for sustainable landscape planning and tree conservation.

In this study, bioclimatic variables in 2100 were created by using 7 Global Climate Models (GCMs) predicted by the Shared Socioeconomic Pathways (SSP) scenario. Using the dismo package of the R program, 19 bioclimatic variables of the year 2100 were generated. The annual mean temperature in 2100 has risen the most compared to the present (2000). Bioclimatic variables can be mainly used in the Species Distribution Model and Ecological Modeling to predict the distribution of species.

In this study, distribution data and environmental information for predicting the potential habitat of amphibians and reptiles in South Korea were compiled. The distribution data of amphibians and reptiles include nationwide surveys conducted by the National Institute of Ecology (seven surveys) and the Korea National Park Service (one survey). The distribution data are based on 57,777 locations for 35 species. Environmental information related to the habitat of amphibians and reptiles was constructed from 19 bioclimatic variables related to climate and four spatial variables related to geographic factors, and 19 bioclimatic variables for the future (2020-2090) were constructed using the results of SSP scenarios. In addition, species distribution models (MaxEnt) were used to predict current and future potential habitat for 28 amphibian and reptile species with more than 50 survey sites. The model validation values for the 28 species ranged from 0.717 to 0.987. These data have the potential to inform conservation strategies in response to climate change by spatially identifying current and future potential habitat for amphibians and reptiles.