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- P-ISSN2765-2203
- E-ISSN2765-2211
- KCI Candidate
Abstract
The Chinese crested tern (Thalasseus bersteini) is one of the most globally endangered species, listed as “Critically Endangered (CE)” on the IUCN Red List, with only approximately 30-49 individuals surviving in the wild. Chinese crested terns were discovered to breed in South Korea for the first time in 2016 while conducting a census on uninhabited islands. The Ministry of Environment has declared the breeding habitat of the Chinese crested terns as “Specified Island” to protect this CE species. However, brown rats (Rattus norvegicus) inhabiting the breeding grounds of the Chinese crested terns and Black-tailed gulls may potentially pose a threat to the breeding of these avian species. Therefore, we conducted a study on the feeding behavior of brown rats involving stable isotope analysis to determine their food sources. Fecal analysis showed that brown rats mainly fed on plants, whereas they scarcely fed on animals, such as insects. In addition, the stable isotope analysis showed that the δ13C values of brown rats, insects, and Indian goosegrasses were approximately –16 to –11‰, whereas the δ13C value of Chinese crested terns that obtained their food from the marine ecosystem was approximately –22 to –18‰. Hence, we conclude that the source of carbon for brown rats on this island is the terrestrial ecosystem. We ruled out the possibility of any direct prey– predator interaction between the brown rat and the Chinese crested tern or Black-tailed gull.
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.