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- P-ISSN2765-2203
- E-ISSN2765-2211
- KCI Candidate
Abstract
This study surveyed the changes in the proportion of Ecological and Natural Map (ENM) grades in Korea, the distribution ratio of ENM 1st-grade areas by region, and the current status of regional public appeals for the five-year period from 2017 to 2021. The nationwide changes in ENM grades revealed an increase in 1st-grade, 3rd-grade, and separately managed areas but a decrease in the ratio of 2nd-grade areas. Nationwide, Gangwon had the highest distribution ratio of 1st-grade areas, at 46.77%, while Gwangju had the lowest, at 0.05%. In the five-year study period, 383 appeals concerning ENM grades were received and processed. Gangwon had the greatest number of appeals, with 96, while Sejong had the fewest, with 1. A significant correlation was observed between the distribution ratio of 1st-grade areas and public appeals.
Abstract
The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.
Abstract
This study analyzed research trends in the field of ecological research. Data were collected based on a keyword search of the SCI, SSCI, and A&HCI databases from January 2002 to September 2022. The seven keywords, including biodiversity, ecology, ecotourism, species, climate change, ecosystem, restoration, wildlife, were recommended by ecological research experts. Word clouds were created for each of the searched keywords, and topic map analysis was performed. Topic map analysis using biodiversity, climate change, ecology, ecosystem, and restoration each generated 10 topics; topic maps analysis using the ecotourism keyword generated 5 topics; and topic map analysis using the wildlife keyword generated 4 topics. Each topic contained six keywords.
Abstract
We analyzed data of endangered mammals in the 1st grade zone of the Ecological and Natural Map of Korea that were obtained through 202 field surveys over six years. Five endangered mammal species were identified including otters, long-tailed gorals, martens, leopard cats, and flying squirrels. The total number of habitat traces collected was 918, of which 897 traces (97.7%) were excrement types. The total surveyed distance was 697.7 km and there were 2,184 grids of 250×250 m each. Of these grids, 441 or 20.2% were confirmed as habitats of endangered mammals. Moreover, we analyzed results of repeated surveys in the same area by converting them into individual one-time surveys, accounting for 23.1% of the total area. The flying squirrel showed a low correlation with the frequency of field surveys but showed many habitats in a specific season. Leopard cats and martens were correlated with the frequency of field surveys. Results of analysis confirm that the grid method used for establishing the Ecological and Natural Map is unsuitable for the habitat division of flying squirrels, otters, leopard cats, and martens, and it does not reflect the actual habitats of these four species. Therefore, we propose that the concept of the habitat grid of species must be reevaluated and improved, specifically for endangered mammals.
Abstract
The Tibetan Plateau is home to the only alpine crane species, the black-necked crane (Grus nigricollis). Conservation efforts are severely hampered by a lack of knowledge on the spatial distribution and breeding habitats of this species. The ecological niche modeling framework used to predict the spatial distribution of this species, based on the maximum entropy and occurrence record data, allowed us to generate a species-specific spatial distribution map in Ladakh, Trans-Himalaya, India. The model was created by assimilating species occurrence data from 486 geographical sites with 24 topographic and bioclimatic variables. Fourteen variables helped forecast the distribution of black-necked cranes by 96.2%. The area under the curve score for the model training data was high (0.98), indicating the accuracy and predictive performance of the model. Of the total study area, the areas with high and moderate habitat suitability for black-necked cranes were anticipated to be 8,156 km2 and 6,759 km2, respectively. The area with high habitat suitability within the protected areas was 5,335 km2. The spatial distribution predicted using our model showed that the majority of speculated conservation areas bordered the existing protected areas of the Changthang Wildlife Sanctuary. Hence, we believe, that by increasing the current study area, we can account for these gaps in conservation areas, more effectively.
