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- P-ISSN2765-2203
- E-ISSN2765-2211
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Abstract
The oriental stork (Ciconia boyciana) is listed as an endangered species internationally. Its resident population has been extirpated in South Korea since 1971. Its predicted historical habitat included forests (54%), rice paddy fields (28%), grasslands (17%), river-streams (less than 1%), and villages (less than 1%) based on pre-extirpation records in a previous study. However, habitat attributes of recently reintroduced oriental storks since 2015 remain unknown. To examine habitat use patterns and home ranges of recently reintroduced oriental storks, 2015-2017 tracking data of 17 individuals were used to analyze their spatial attributes with a Kernel Density Estimate method and breeding status. Their habitat use patterns from peripheral to core areas were highly associated with increasing rice paddy fields (26%) and decreasing forested areas (55%). Scale-dependent home ranges were 51% smaller for breeders than for non-breeders on average. Our study results highlight that the habitat use pattern of reintroduced oriental storks seems to be comparable to the historical pattern where the used area is likely to be more centralized for breeders than for non-breeders in South Korea. Furthermore, the direction of habitat management for oriental storks should focus on biodiversity improvement of rice paddy fields with chemical free cultivation and irrigation.
Abstract
Predictions of suitable habitat areas can provide important information pertaining to the risk assessment and management of alien plants at early stage of their establishment. Here, we predict the invasion potential of Muhlenbergia capillaris (pink muhly) in South Korea using five bioclimatic variables. We adopt four models (generalized linear model, generalized additive model, random forest (RF), and artificial neural network) for projection based on 630 presence and 600 pseudo-absence data points. The RF model yielded the highest performance. The presence probability of M. capillaris was highest within an annual temperature range of 12 to 24°C and with precipitation from 800 to 1,300 mm. The occurrence of M. capillaris was positively associated with the precipitation of the driest quarter. The projection map showed that suitable areas for M. capillaris are mainly concentrated in the southern coastal regions of South Korea, where temperatures and precipitation are higher than in other regions, especially in the winter season. We can conclude that M. capillaris is not considered to be invasive based on a habitat suitability map. However, there is a possibility that rising temperatures and increasing precipitation levels in winter can accelerate the expansion of this plant on the Korean Peninsula.
Abstract
Living modified organisms (LMOs) are managed by seven government agencies according to their use in South Korea. The Ministry of Environment is responsible for LMOs used for environmental remediation. This study aimed to develop guidelines for assessing potential risks posed by transgenic plants used for remediation to insect ecosystems by investigating arthropod communities in sunflower fields. A total of 2,350 insects and spiders belonging to 134 species of 10 orders and 71 families were collected from sunflower fields over four growth stages ranging from anthesis to seed maturity. At the R3 phase of flower-bud formation, Chironomidae sp. of a decomposer insect guild presented the highest density, while Apis mellifera of a pollinator guild was the most abundant in the R5.8 phase of flowering. During the R7 seed-filling phase and the R9 phase of seed maturity, herbivorous Pochazia shantungensis predominated. During the R9 phase, richness and diversity indices of arthropod communities were distinctly lower whereas their dominance indices were significantly higher than those at other phases. In addition, the composition of arthropod communities was strongly correlated not only with the sampling date, but also with the sampling method depending on the growth stage of sunflowers. Our results suggest that appropriate sampling timing and methods should be considered in advance and that long-term field trials that cover a variety of environmental conditions should be carried out to evaluate potential risks to insect ecosystems.
Abstract
Most of the islands of Korea are distributed in the South and West Sea, and it consists of independent small stream. As a result, the fish community that inhabits the island's stream is isolated from the mainland and other island. This study utilized a Self-Organizing Map (SOM) and a random forest model to analyze the relationship between environmental variables and fish communities inhabiting islands in South Korea. Through the SOM analysis, the fish communities were divided into three clusters, and there were differences in biotic and abiotic factors between these groups. Cluster I consisted of sites with relatively larger island areas and a higher number of species and population. It was found that 15 out of 16 indicator species were included. Meanwhile, the remaining clusters had fewer species and populations. Cluster II, especially, showed the lowest impact from physical variables such as water width and depth. As a result of predicting the species richness using the random forest model, physical variables in habitats, such as stream width and water depth, had a relatively higher importance on species richness. On the other hand, forest area was the most important variables for predicting Shannon diversity, followed by maximum water depth, and gravel. The results suggest that this study can be used as basic data for establishing a stream ecosystem management strategy in terms of conservation and protection of biological resources in streams of islands.
Abstract
Natural habitats of the Korean long-tailed goral (Naemorhedus caudatus) have been fragmented by anthropogenic activities in South Korea in the last decades. Here, the individual identity, genetic variation, and population differentiation of the endangered species were examined via the multiple-tube approach using a non-invasive genotyping method. The average number of alleles was 3.16 alleles/locus for the total population. The Yanggu population (1.66) showed relatively lower average number of alleles than the Inje population (3.67). Of the total 19 alleles, only seven (36.8%) alleles were shared by the two populations. Using five polymorphic out of six loci, four and six different goral individuals from the captive Yanggu (n=24) and the wild Inje (n=28) population were identified, respectively. The allele distribution was not identical between the two populations (Fisher’s exact test: P<0.01). A considerably low migration rate was detected between the two populations (no. of migrants after correction for size=0.294). Additionally, the F statistics results indicated significant population differentiation between them, however, quite low ( FST=0.327, P<0.01). The posterior probabilities indicated that the two populations originated from a single panmictic population (P=0.959) and the assignment test results designated all individuals to both populations with nearly equal likelihood. These could be resulted from moderate population differentiation between the populations. No significant evidence supported recent population bottleneck in the total Korean goral population. This study could provide us with useful population genetic information for conservation and management of the endangered species.’
Abstract
Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep- learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.
Abstract
The objective of this study was to provide basic data for the conservation of wetland ecosystems in the Civilian Control Zone and the management of Yongyangbo wetlands in South Korea. Yongyangbo wetlands have been designated as protected areas. A field survey was conducted across five sessions between April 2019 and August of 2019. A total of 248 taxa were identified during the survey, including 72 families, 163 genera, 230 species, 4 subspecies, and 14 varieties. Their life-forms were Th (therophytes) - R5 (non-clonal form) - D4 (clitochores) - e (erect form), with a disturbance index of 33.8%. Three taxa of rare plants were detected: Silene capitata Kom. and Polygonatum stenophyllum Maxim. known to be endangered species, and Aristolochia contorta Bunge, a least-concern species. S. capitata is a legally protected species designated as a Class II endangered species in South Korea. A total of 26 taxa of naturalized plants were observed, with a naturalization index of 10.5%. There was one endemic plant taxon (Salix koriyanagi Kimura ex Goerz). In terms of floristic target species, there was one taxon in class V, one taxon in Class IV, three taxa in Class III, five taxa in Class II, and seven taxa in Class I. Three invasive alien species (Ambrosia trifida L., Ambrosia artemisiifolia L., and Humulus japonicus Siebold & Zucc) were observed. For continuous conservation of Yongyangbo Wetlands, it is necessary to remove invasive alien plants and block the inflow of non-point pollutants.
Abstract
In this study, we investigated the water quality and fish community of the Gudam Wetland, a riverine wetland in the middle-upper reaches of the Nakdong River, during March-October 2020. The main results were as follows: average annual flow rate: 45.0±23.7 m3/s, flow velocity: 0.4±0.3 m/s, water depth: 1.4±0.4 m, water temperature: 17.5±0.8°C, pH: 7.8±0.2, electrical conductivity: 121.6±19.0 µs/cm, dissolved oxygen concentration: 11.4±0.9 mg/L, suspended solids concentration: 3.8±2.0 mg/L, and the water quality was classified as Ia (very good). A total of 754 individual fish belonging to 4 orders, 7 families, and 19 species were investigated. Cyprinidae was the dominant group, with 13 species. The dominant species was Zacco platypus (39.3%), followed by Pseudogobio esocinus (17.5%). There were 8 (42.1%) endemic Korean species and 1 exotic species, Micropterus salmoides. Four species were carnivores, six were insectivores, and nine were omnivores. Regarding tolerance to environmental changes, 6 species were tolerant, 11 had intermediate tolerance, and 2 were sensitive. Fish community analysis revealed dominance of 0.57, diversity of 2.04, evenness of 0.69, and richness of 2.72, indicating a diverse and stable fish community. The fish assessment index showed that the assessment class was B (average 62.5), which was higher than that of major streams of the Nakdong River (class C). For sustainable conservation of the Gudam Wetland, management strategies such as minimizing aggregate collection and preventing inflow of non-point pollutants are required.
