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    ์ค‘๊ตญ ๋™๋ถ์ง€์—ญ์—์„œ ์ƒˆ๋กœ ํ™•์‚ฐ๋œ ๊ณ ๋ผ๋‹ˆ์˜ ์ž ์žฌ์  ์„œ์‹์ง€์™€ ์ง€์—ญ์ฃผ๋ฏผ๋“ค์˜ ์•ผ์ƒ๋™๋ฌผ ์ธ์‹์— ๊ด€ํ•œ ์—ฐ๊ตฌ ๋ฐ ์•„๋ฌด๋ฅดํ˜ธ๋ž‘์ด ์™€ ์•„๋ฌด๋ฅดํ‘œ๋ฒ” ๋ณด์ „์— ๊ฐ–๋Š” ์˜๋ฏธ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ˆ˜์˜๊ณผ๋Œ€ํ•™ ์ˆ˜์˜ํ•™๊ณผ, 2023. 2. ์กฐ์ œ์—ด.Prey animal and local communities awareness are one of the important aspects for big cats conservation. There are multiple small populations of tigers (Panthera tigris) and leopards (Panthera pardus) across Asia due to the rapid expansion of human populations and the subsequent development of human-dominated landscapes. The habitat patches in northeast China and southwest Primorye of Russia also retain the last population of Amur leopards (Panthera pardus orientalis) and a metapopulation of Amur tiger (Panthera tigris altaica). This region recently became the new habitat for a deer species, water deer (Hydropotes inermis), which can become a potential prey species for the big cats; having the baseline knowledge of the water deer and the attitudes of the local community towards wildlife may have important implications for the big cats conservation. We applied camera trapping, genetic analysis, a species distribution model, and a questionnaire survey to acquire the baseline information. It is challenging but crucial to combine multiple research techniques when surveying wildlife. The ecology of wildlife, their interactions with the environment, and how the landscape provides a habitat for wildlife are all important aspects of wildlife conservation. The presence of humans in wildlife habitats is crucial to conservation efforts' success and long-term viability. This study focuses on one aspect of wildlife research, but more is needed because the situation surrounding wildlife conservation is complicated. Understanding wildlife needs and how people and wildlife interact in the ecosystem will be essential. The advancement of wildlife monitoring techniques, such as non-invasive genetic sampling, camera trapping, and traditional transect surveys, offers an effective method for gathering accurate information about wildlife. When evaluating wildlife habitat, landscape ecology methods offer a broad perspective. Species distribution models (SDM) can use landscape data, information about human influence, and species ecology information to predict critical conservation regions. Well-designed questionnaire surveys can identify people's wildlife interactions. Range expansion for wildlife occurs due to human activity and climate change. There is a need for knowledge and an updated management approach. Since 2019, the water deer (Hydropotes inermis), a small size (15 kg) deer species, has been recognized as a new expanded species in northeast China and the far east of Russia. With several different deer species, high diversity of wildlife exists in the expanded region, including the Amur tiger and Amur leopard. The newly expanded water deer have a high reproductive rate, may serve as a potential prey animal for big cats influencing other species, and may even interact with local people. In this dissertation research, I used camera trapping, species distribution models, and questionnaire surveys to assess the northward movement of water deer in northeast China, focusing on the Tumen transboundary region between northeast China, the Russian Far East, and North Korea. My research had three main goals:1) to confirm the species expansion, 2) to assess the habitat, and 3) to assess people's attitudes. I also tried to draw the implications for the big cat conservation from the results. In order to accomplish the goals, I collaborate with regional partners in the research area, such as the local forestry department, Yanbian University, Beijing Normal University, Wildlife Conservation Society, and others, to gather ecological data, conduct household surveys, and analyze landscape data. The research results may provide management guidance for the newly expanded deer species and contribute to the conservation of endangered big cats. In chapter 1, I employed camera traps, ecological studies, and genetic techniques to identify the expanding deer species and collected information on their range. The range of water deer has extended northward by at least 500 km from its previous distribution limit, and this population shares a tight evolutionary relationship with Korean water deer. In chapter 2, I identified the appropriate environment and figured out potential expansion pathways for the water deer. MaxEnt model was used to access the habitat. Because environmental factors can be evaluated through their contribution to the model, I discovered that the suitable water deer habitat on the east coasts of the Korean Peninsula (Hamyong-namdo patch) and west coast of the Korean Peninsula (Pytongan-namdo patch) and the newly expanded region along the border between China, North Korea, and the Russian Far East (Hunchun patch). Elevation, wetland region, the availability of water sources, and farmland habitat were significant factors that helped water deer choose their home in the new area. Three main connection routes were estimated among habitat patches. The east route was from Hamyong namdo cross Ryangando and Hamyong bukdo to Hunchun; the middle route was from Pyongan namdo cross Chagang do to Baishan, Atu, Helong, Longjing to Hunchun; The west route was from Pyongan namdo to Chagang do, Baishan, Antu, Dunhua, Wangqing to Hunchun. The predicted habitat connections may serve as the water deer dispersal routes in the past, and further dispersal trends may be predicted through the modeling results. Predators, such as tigers and leopards, may also use the similar routes for their future dispersal. In Chapter 3, I also investigated residents' attitudes toward wildlife using a questionnaire survey, which may have ramifications for the new extension of water deer management. I discovered that people's attitudes regarding wildlife are influenced by their age, gender, education, and contact with wildlife. Residents usually had neutral sentiments toward large animals, but they had very negative opinions against wild boar, especially if they had suffered losses from crop raiding. It will be crucial to be alert of any potential conflict in the new expansion territory of the water deer, given that the species may induce severe crop raiding in the area in the future. All of these details will be crucial and useful for managing and conserving the newly expanded water deer population. This study illustrates how a scientific working process brings together wildlife, habitat, and the local community when gaining access to and conserving newly expanded species in new ranges. This study results may have important implications to tiger and leopard conservation, both positive and negative. First positive implication is that the northward expansion of water deer into the newly established big cat range in northeast region of China may have positive effects on big cat populations by increasing prey animal diversity in their habitat. Secondly, we forecasted the potential corridors for water deer, which can be used in the future for big cats as a potential habitat or dispersal routes because of the potential existence of new prey species in the connection areas; Finally, the data on the local people's attitudes towards wildlife can help building strategic plans for future tiger and leopard conservation education and prey management. However, the results may have negative implications for big cat population; for example, potential introduction of novel diseases or pathogens of ungulates to the expanded region, creation of potential disturbance or competition in the wildlife community of the expanded region, bringing about a new type of wildlife-human conflict in the region etc.๋จน์ด๋™๋ฌผ์˜ ๋ถ„ํฌ์™€ ์ง€์—ญ ์ฃผ๋ฏผ๋“ค์˜ ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•œ ์ธ์‹๊ณผ ํƒœ๋„๋Š” ๋Œ€ํ˜• ๊ณ ์–‘์ด๊ณผ ๋™๋ฌผ ๋ณด์กด์— ์žˆ์–ด์„œ ์ค‘์š”ํ•œ ์ธก๋ฉด ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ์ž์—ฐ ๊ฒฝ๊ด€์— ๋Œ€ํ•œ ์ธ๊ฐ„์˜ ์ง€๋ฐฐ์  ์˜ํ–ฅ๋ ฅ์ด ๊ธ‰์†ํžˆ ํ™•์žฅ๋จ์— ๋”ฐ๋ผ ์•„์‹œ์•„์— ์„œ์‹ํ•˜๊ณ  ์žˆ๋Š” ํ˜ธ๋ž‘์ด์™€ ํ‘œ๋ฒ” ๊ฐœ์ฒด๊ตฐ์ด ์†Œ์‹ค๋˜๊ฑฐ๋‚˜ ๋˜๋Š” ๊ฐœ์ฒด๊ตฐ ๊ณ ๋ฆฝ๊ณผ ํŒŒํŽธํ™”๋ฅผ ์ดˆ๋ž˜ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ค‘๊ตญ ๋™๋ถ์ง€์—ญ๊ณผ ๊ทน๋™๋Ÿฌ์‹œ์•„ ๋‚จ์„œ๋ถ€ ํ•ด์•ˆ์ง€์—ญ ์ผ๋ถ€์—๋Š” ์•„์ง ์•„๋ฌด๋ฅดํ˜ธ๋ž‘์ด(Panthera tigris altaica)์™€ ์•„๋ฌด๋ฅดํ‘œ๋ฒ”(Panthera pardus orientalis)์˜ ์„œ์‹์ง€๊ฐ€ ๋ณด์ „๋˜์–ด ์žˆ๋‹ค. ์ด๊ณณ์— ์‚ด๊ณ  ์žˆ๋Š” ์•„๋ฌด๋ฅดํ‘œ๋ฒ”์€ ํ˜„์žฌ ์•ผ์ƒ์—์„œ ์ƒ์กดํ•˜๋Š” ๋งˆ์ง€๋ง‰ ๊ฐœ์ฒด๊ตฐ์ด๋ฉฐ ์•„๋ฌด๋ฅดํ˜ธ๋ž‘์ด๋Š” ์ค‘๊ตญ ๋™๋ถ์ง€์—ญ์˜ ๋ฉ”ํƒ€ ๊ฐœ์ฒด๊ตฐ์ด ๋œ๋‹ค. ์ด ์ง€์—ญ์—๋Š” ์ตœ๊ทผ ์‚ฌ์Šด๊ณผ ๋™๋ฌผ์ข…์ธ ๊ณ ๋ผ๋‹ˆ(Hydropotes inermis)๊ฐ€ ์ž์—ฐ์ ์ธ ์„œ์‹์ง€ ํ™•์‚ฐ์— ์˜ํ•ด ์ƒˆ๋กœ ์„œ์‹ํ•˜๊ฒŒ ๋˜์˜€์œผ๋ฉฐ, ์ด๋“ค์€ ๋ฏธ๋ž˜ ํฐ๊ณ ์–‘์ด๊ณผ๋™๋ฌผ(ํ˜ธ๋ž‘์ด์™€ ํ‘œ๋ฒ”)์˜ ์ž ์žฌ์  ๋จน์ด๊ฐ€ ๋  ๊ฐ€๋Šฅ์„ฑ์ด ํฌ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๊ณ ๋ผ๋‹ˆ ๊ฐœ์ฒด๊ตฐ ํ™•์‚ฐ์— ๋Œ€ํ•œ ๊ธฐ์ดˆ ์ •๋ณด์™€ ์ง€์—ญ์ฃผ๋ฏผ๋“ค์˜ ์•ผ์ƒ๋™๋ฌผ ์ธ์‹์— ๊ด€ํ•œ ์ •๋ณด๋Š” ๋Œ€ํ˜•๊ณ ์–‘์ด๊ณผ๋™๋ฌผ์˜ ๋ณด์ „์— ์žˆ์–ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ ์™ธ์„  ์นด๋ฉ”๋ผ ๊ธฐ๋ฒ•, ์œ ์ „์ž ๋ถ„์„ ๊ธฐ๋ฒ•, ์ข… ๋ถ„ํฌ ๋ชจ๋ธ ๋ฐ ์„ค๋ฌธ์กฐ์‚ฌ ๋ฐฉ์‹์„ ํ†ตํ•ด ์ด๋Ÿฌํ•œ ์ •๋ณด๋ฅผ ์–ป์„ ๋ชฉ์ ์œผ๋กœ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ์•ผ์ƒ๋™๋ฌผ๊ณผ ์„œ์‹์ง€์˜ ๋ณดํ˜ธ๋Š” ์ธ๊ฐ„์˜ ๋ณต์ง€์™€ ์ƒ์กด์„ ์œ„ํ•ด์„œ๋„ ์ค‘์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ธ๊ตฌ ์ฆ๊ฐ€๋Š” ์ž์›์ˆ˜์š” ์ฆ๊ฐ€์™€ ๋งž๋ฌผ๋ ค ์ธ๊ฐ„๊ณผ ์•ผ์ƒ๋™๋ฌผ์ด ๊ณต์กดํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์ค‘์š”ํ•œ ๋„์ „์  ๋ฌธ์ œ๋ฅผ ์ œ๊ธฐํ•œ๋‹ค. ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•œ ์กฐ์‚ฌ ๋ฐ ๋ณดํ˜ธ์— ๊ด€ํ•œ ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋Š” ๊ฐœ๊ฐœ์˜ ํŠน์ • ๋ถ„์•ผ์— ์„ธ๋ถ„ํ™”๋˜์–ด ์žˆ๋‹ค. ์—ฌ๋Ÿฌ ๋ถ„์•ผ๋ฅผ ์•„์šฐ๋ฅด๋Š” ํ†ตํ•ฉ์  ์—ฐ๊ตฌ๋ฐฉ์‹์ด ์‰ฝ์ง€๋Š” ์•Š์ง€๋งŒ ์•ผ์ƒ๋™๋ฌผ์˜ ๋ณด์ „์— ์žˆ์–ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์•ผ์ƒ๋™๋ฌผ๊ณผ ๊ทธ ์ƒํƒœํ™˜๊ฒฝ๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ, ๊ทธ๋ฆฌ๊ณ  ๊ฒฝ๊ด€์ด ์•ผ์ƒ๋™๋ฌผ์—๊ฒŒ ์„œ์‹์ง€๋ฅผ ์ œ๊ณตํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ๋ชจ๋‘ ์•ผ์ƒ๋™๋ฌผ ๋ณด์ „์˜ ์ค‘์š”ํ•œ ์ธก๋ฉด์ด๋‹ค. ์•ผ์ƒ๋™๋ฌผ ์„œ์‹์ง€์— ์‚ฌ๋Š” ์ฃผ๋ฏผ๋“ค์ด ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•ด ๊ฐ–๋Š” ํƒœ๋„ ๋ฐ ๋ณด์ „์— ๋Œ€ํ•œ ์ธ์‹์€ ๋ฏธ๋ž˜์˜ ๋ณด์ „ ํ™œ๋™์ด ์„ฑ๊ณตํ•  ์ˆ˜ ์žˆ๋Š”์ง€ ์—ฌ๋ถ€์™€ ์žฅ๊ธฐ์ ์ธ ์ง€์† ๊ฐ€๋Šฅ์„ฑ์— ์žˆ์–ด ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๋ณด์ „์— ๊ด€ํ•œ ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋Š” ์•ผ์ƒ๋™๋ฌผ์˜ ํŠน์ • ์ธก๋ฉด์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ์ง€๋งŒ ์•ผ์ƒ๋™๋ฌผ์ด ์ง๋ฉดํ•œ ๋ฌธ์ œ๋Š” ๋‹ค์–‘ํ•˜๊ณ  ๋ณต์žกํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ฌธ์ œ๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ณ ๋ คํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•œ ์˜๋ฏธ๋ฅผ ์ง€๋‹Œ๋‹ค. ์ข… ์ƒํƒœํ•™, ๊ฒฝ๊ด€์ƒํƒœํ•™, ์‚ฌํšŒํ•™์  ์ด๋ก ์€ ๋ชจ๋‘ ๋ณด์ „์ƒ๋ฌผํ•™์˜ ์ค‘์š”ํ•œ ๋’ท๋ฐ›์นจ์ด ๋˜์—ˆ๋‹ค. ๋น„์นจ์Šต์  ์œ ์ „์ž ์‹œ๋ฃŒ ์ˆ˜์ง‘๊ณผ ๋ถ„์„, ์ ์™ธ์„  ์นด๋ฉ”๋ผ ๋ฐ ์ „ํ†ต์ ์ธ ์ƒํƒœ ์กฐ์‚ฌ์™€ ๊ฐ™์€ ์•ผ์ƒ๋™๋ฌผ ๋ชจ๋‹ˆํ„ฐ๋ง ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์€ ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ์ •๋ณด ์ˆ˜์ง‘์„ ์œ„ํ•œ ๊ธฐ๋ณธ ์ˆ˜๋‹จ์„ ์ œ๊ณตํ•œ๋‹ค. ์•ผ์ƒ๋™๋ฌผ ์„œ์‹์ง€๋ฅผ ํ‰๊ฐ€ํ•  ๋•Œ ๊ฒฝ๊ด€์ƒํƒœํ•™ ๋ถ„์•ผ๋Š” ํšจ๊ณผ์ ์ธ ํ‰๊ฐ€๋„๊ตฌ๋ฅผ ์ œ๊ณตํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ข…๋ถ„ํฌ๋ชจ๋ธ(Species distribution model)์€ ๊ฒฝ๊ด€์ฒ™๋„์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธ๊ฐ„ํ™œ๋™์˜ ์˜ํ–ฅ, ํ™˜๊ฒฝ๋ฐฐ๊ฒฝ ์ •๋ณด ๋ฐ ์ข… ์ƒํƒœ์ •๋ณด๋ฅผ ๊ฒฐํ•ฉํ•˜์—ฌ ์ค‘์ ๋ณดํ˜ธ์ง€์—ญ์„ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ๋˜ํ•œ ์ƒํƒœ ๋„คํŠธ์›Œํฌ ์—ฐ๊ฒฐ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์ฃผ์š” ์„œ์‹์ง€ ํŒจ์น˜ ๊ฐ„์˜ ์—ฐ๊ฒฐ์„ ๊ณ„์‚ฐํ•˜๊ณ  ๊ฒฝ๊ด€ ๊ทœ๋ชจ ์„œ์‹์ง€ ์ •๋ณด๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ์„ค๋ฌธ์กฐ์‚ฌ ๊ธฐ๋ฒ•์€ ์ธ๊ฐ„๊ณผ ์•ผ์ƒ๋™๋ฌผ์˜ ์ถฉ๋Œ ๋ฐ ์ฃผ๋ฏผ ์ธ์‹ ์ƒํƒœ๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ์ค‘์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋‹ค. 2019๋…„์— ๊ณ ๋ผ๋‹ˆ๊ฐ€ ํ˜„์žฌ ์•„๋ฌด๋ฅดํ˜ธ๋ž‘์ด ๋ฐ ์•„๋ฌด๋ฅดํ‘œ๋ฒ”์ด ๋ถ„ํฌํ•˜๋Š” ๋‘๋งŒ๊ฐ• ํ•˜๋ฅ˜ ์ง€์—ญ์œผ๋กœ ํ™•์‚ฐ๋œ ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ๋˜์—ˆ๋‹ค. ๊ณ ๋ผ๋‹ˆ๋Š” ๋ฒˆ์‹๋ฅ ์ด ๋†’์•„ ๋ฏธ๋ž˜ ๋Œ€ํ˜•๊ณ ์–‘์ด๊ณผ ๋™๋ฌผ์˜ ๋จน์ด๊ฐ€ ๋  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์ง€๋งŒ, ๋™์‹œ์— ๋‹ค๋ฅธ ์ƒ๋ฌผ์— ์ผ์ •ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ ์ง€์—ญ์ฃผ๋ฏผ๊ณผ ์ถฉ๋Œํ•  ๊ฐ€๋Šฅ์„ฑ๋„ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฃผ๋กœ ์ ์™ธ์„  ์นด๋ฉ”๋ผ, ์ข… ๋ถ„ํฌ๋ชจ๋ธ ๋ฐ ์„ค๋ฌธ์กฐ์‚ฌ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ค‘๊ตญ ๋™๋ถ์ง€์—ญ ๊ณ ๋ผ๋‹ˆ(๋Œ€ํ˜•๊ณ ์–‘์ด๊ณผ๋™๋ฌผ์˜ ์ž ์žฌ์  ๋จน์ด)์˜ ๋ถ์ชฝ ํ™•์‚ฐ ๋„ˆ๋น„๋ฅผ ํ‰๊ฐ€ํ–ˆ์œผ๋ฉฐ ์—ฐ๊ตฌ์˜ ์ค‘์  ์ง€์—ญ์€ ์ค‘๊ตญ ๋™๋ถ๋ถ€๊ณผ ๊ทน๋™๋Ÿฌ์‹œ์•„์˜ ๋ถํ•œ ์ ‘๊ฒฝ์ง€์—ญ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๋ชฉํ‘œ๋Š” ์ข…์˜ ํ™•์žฅ์„ ํ™•์ธํ•˜๊ณ  ์„œ์‹์ง€๋ฅผ ํ‰๊ฐ€ํ•˜๋ฉฐ ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•œ ์ง€์—ญ์ฃผ๋ฏผ์˜ ํƒœ๋„๋ฅผ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์—ฐ๊ตฌ ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด ์ง€์—ญ ์ž„์—…๋ถ€์„œ, ์—ฐ๋ณ€๋Œ€ํ•™, ๋ถ๊ฒฝ์‚ฌ๋ฒ”๋Œ€ํ•™, ๊ตญ์ œ์•ผ์ƒ์ƒ๋ฌผ๋ณดํ˜ธํ•™ํšŒ ๋ฐ ๊ธฐํƒ€ ๊ด€๋ จ ๋ถ„์•ผ์˜ ์ง€์—ญ ํŒŒํŠธ๋„ˆ์™€ ํ˜‘๋ ฅํ•˜์—ฌ ์ƒํƒœ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๊ณ  ๊ฐ€๊ตฌ ์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ณ  ๊ฒฝ๊ด€ ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„์„ํ–ˆ๋‹ค. ์ ์™ธ์„  ์นด๋ฉ”๋ผ, ์ƒํƒœํ•™์  ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘, ์œ ์ „์ž ์‹œ๋ฃŒ ์ˆ˜์ง‘๊ณผ ๋ถ„์„์„ ํ†ตํ•ด ์ข…์„ ๋ณด๋‹ค ์ •ํ™•ํ•˜๊ฒŒ ์‹๋ณ„ํ•˜๊ณ  ๋ถ„ํฌ ๋ฒ”์œ„๋ฅผ ์—…๋ฐ์ดํŠธํ–ˆ์œผ๋ฉฐ, ๊ณ ๋ผ๋‹ˆ์˜ ๋ถ„ํฌ๊ฐ€ ์ด์ „์— ๊ธฐ๋ก๋œ ๋ฒ”์œ„์—์„œ ์ตœ์†Œ 500km ๋ถ์ชฝ์œผ๋กœ ํ™•์‚ฐ๋˜์—ˆ์œผ๋ฉฐ, ๋‚จํ•œ ๊ณ ๋ผ๋‹ˆ์˜ ์œ ์ „์  ํŠน์„ฑ๊ณผ ๋” ๋ฐ€์ ‘ํ•œ ๊ด€๋ จ์ด ์žˆ์Œ์„ ํ™•์ธํ–ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ƒˆ๋กœ์šด ์„œ์‹์ง€์—ญ์œผ๋กœ ํ™•์‚ฐ๋œ ๊ณ ๋ผ๋‹ˆ ๋ณดํ˜ธ ๋ฐ ๊ด€๋ฆฌ์— ๋Œ€ํ•œ ๊ธฐ๋ณธ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๊ณ  ๋Œ€ํ˜•๊ณ ์–‘์ด๊ณผ๋™๋ฌผ์˜ ๋ณด์ „์— ๋„์›€์„ ์ค„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค (์ œ1์žฅ). ์ ์ ˆํ•œ ํ™˜๊ฒฝ ๋ณ€์ˆ˜๋ฅผ ๊ฒฐ์ •ํ•œ ํ›„ MaxEnt ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์„œ์‹์ง€๋ฅผ ๋ถ„์„ํ•˜๋ฉด ํ™˜๊ฒฝ ๋ณ€์ˆ˜๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ  ์ตœ์ข…์ ์œผ๋กœ ํŠน์ • ์ข…์— ์ ํ•ฉํ•œ ์„œ์‹์ง€๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œ๋ฐ˜๋„ ์„œํ•ด์•ˆ์—์„œ ์ค‘๊ตญ ๋žด์˜ค๋‹์„ฑ๊นŒ์ง€, ๋™ํ•ด์•ˆ์—์„œ ๋Ÿฌ์‹œ์•„ ์šฐ์ˆ˜๋ฆฌ๊ฐ•๊นŒ์ง€ ๋ป—์–ด ์žˆ๋Š” ๊ณ ๋ผ๋‹ˆ ์„œ์‹์— ์ ํ•ฉํ•œ ๋„“์€ ์ง€์—ญ์„ ๋ฐœ๊ฒฌํ–ˆ์œผ๋ฉฐ ์„œ์‹์ง€ ์—ฐ๊ฒฐ ๋ถ„์„์„ ํ†ตํ•ด ๊ณ ํ’ˆ์งˆ ์„œ์‹์ง€ ํ”Œ๋ผํฌ๋ฅผ ์—ฐ๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ์ƒํƒœํšŒ๋ž‘์„ ๋„์ถœํ•˜์˜€๋‹ค(์ œ2์žฅ). ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•œ ํ˜„์ง€ ์ฃผ๋ฏผ์˜ ํƒœ๋„๋„ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ํ™•์ธํ–ˆ๋Š”๋ฐ, ์ด๋Š” ํ–ฅํ›„ ๊ณ ๋ผ๋‹ˆ ํ™•์‚ฐ์—๋„ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•œ ์ง€์—ญ์ฃผ๋ฏผ๋“ค์˜ ํƒœ๋„๊ฐ€ ์—ฐ๋ น, ์„ฑ๋ณ„, ๊ต์œก ๋ฐ ์•ผ์ƒ๋™๋ฌผ๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ๊ณผ ๊ฐ™์€ ์š”์ธ๊ณผ ๊ด€๋ จ์ด ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ–ˆ๋‹ค. ์šฐ๋ฆฌ์˜ ์กฐ์‚ฌ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด ์ง€์—ญ์ฃผ๋ฏผ๋“ค์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋Œ€ํ˜• ์œก์‹๋™๋ฌผ์— ๋Œ€ํ•ด์„œ๋Š” ์ค‘๋ฆฝ์ ์ด์ง€๋งŒ ๋ฉง๋ผ์ง€์— ๋Œ€ํ•ด ๋ถ€์ •์  ์ธ์‹์„ ๊ฐ–๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๋†์ž‘๋ฌผ ํ”ผํ•ด๋ฅผ ์ž…์€ ๊ฒฝํ—˜์ด ์žˆ๋Š” ๊ฐ€์กฑ์€ ๋ฉง๋ผ์ง€์— ๋Œ€ํ•œ ๋ถˆ๋งŒ์ด ๋งค์šฐ ๋†’๋‹ค. ๊ฒฝ์ œ์  ์ˆ˜์ž… ์ˆ˜์ค€๊ณผ ์ˆ˜์ž…์›๋„ ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•œ ์ง€์—ญ์‚ฌํšŒ์˜ ํƒœ๋„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ์ด๋Ÿฌํ•œ ์ •๋ณด๋Š” ํ˜ธ๋ž‘์ด, ํ‘œ๋ฒ” ๋ฐ ๊ทธ ๋จน์ด๋™๋ฌผ์˜ ๋ณดํ˜ธ ๋ฐ ๊ด€๋ฆฌ์ „๋žต ์ˆ˜๋ฆฝ์— ์žˆ์–ด ์ค‘์š”ํ•œ ์ •๋ณด๊ฐ€ ๋  ์ˆ˜ ์žˆ๋‹ค(์ œ3์žฅ). ๋ณธ ์—ฐ๊ตฌ๋Š” ์•„๋ฌด๋ฅดํ˜ธ๋ž‘์ด์™€ ํ‘œ๋ฒ”์˜ ๊ธฐ์กด ์„œ์‹์ง€์— ์ƒˆ๋กœ ํ™•์‚ฐ๋œ ์œ ์ œ๋ฅ˜์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์„œ์‹์ง€ ํ‰๊ฐ€์™€ ์˜ˆ์ธก์„ ํ†ตํ•ด ์ž ์žฌ์  ์„œ์‹์ง€ ํŒจ์น˜ ๋ฐ ์ƒํƒœํšŒ๋ž‘ ์ •๋ณด๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ˜ธ๋ž‘์ด์™€ ํ‘œ๋ฒ”์—๊ฒŒ ์ง์ ‘์ ์ธ ์ƒ์กด์š”์†Œ๋Š” ๋จน์ด๋™๋ฌผ์ด๊ธฐ ๋•Œ๋ฌธ์—, ์ด ์ž ์žฌ์  ๋จน์ด๋™๋ฌผ์˜ ๋ถ„ํฌ์ƒํ™ฉ, ์„œ์‹์ง€์™€ ์ƒํƒœํšŒ๋ž‘์— ๋Œ€ํ•ด ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€ ๊ทธ ํฌ์‹์ž์˜ ๋ณด์ „๊ณผ ๊ด€๋ฆฌ์—๋„ ์ค‘์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ํ˜ธ๋ž‘์ด์™€ ํ‘œ๋ฒ”์˜ ์ž ์žฌ์  ๋จน์ด๋™๋ฌผ์˜ ์„œ์‹์ง€์™€ ํšŒ๋ž‘์€ ๊ทธ ํฌ์‹์ž์—๊ฒŒ๋„ ์ค‘์š”ํ•œ ์„œ์‹์ง€ ๋ฐ ํšŒ๋ž‘ ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋ฏธ๋ž˜ ํ˜ธ๋ž‘์ด์™€ ํ‘œ๋ฒ” ๋ณดํ˜ธ๊ตฌ์—ญ ์„ค๊ณ„์— ์žˆ์–ด ์ด๋Ÿฌํ•œ ์ •๋ณด๋ฅผ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ง€์—ญ์‚ฌํšŒ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์•ผ์ƒ๋™๋ฌผ์— ๋Œ€ํ•œ ํ˜„์ง€์ธ๋“ค์˜ ํƒœ๋„์™€ ์•ผ์ƒ๋™๋ฌผ๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ์— ๊ด€ํ•œ ์ •๋ณด๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Š” ์•ผ์ƒ๋™๋ฌผ ๋ณดํ˜ธ์— ๋Œ€ํ•œ ์ฃผ๋ฏผ๋“ค์˜ ์ง€์›์„ ํ™•๋ณดํ•˜๊ณ  ์‚ด์•„์žˆ๋Š” ๋™๋ฌผ์— ๋Œ€ํ•œ ๊ฐ„์„ญ์„ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์ค‘์š”ํ•œ ์ •๋ณด ์ž์›์ด ๋  ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์•ผ์ƒ๋™๋ฌผ ์ƒํƒœํ•™, ๊ฒฝ๊ด€์ƒํƒœํ•™ ๋ฐ ์‚ฌํšŒํ•™์  ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์„ ํ†ตํ•ฉํ•œ ์—ฐ๊ตฌ ์‚ฌ๋ก€๋กœ ๋ฏธ๋ž˜์˜ ๋‹ค๋ฅธ ์ข… ๋ณดํ˜ธ์— ์ผ์ •ํ•œ ์‹œ๋ฒ” ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.General Backgrounds 1 Amur tiger (Panthera tigris altaica) 3 Amur leopard (Panthera pardus orientalis) 6 The conservation of Amur tiger and amur leopard 8 Water deer (Hydropotes inermis) 12 Purpose of the research 17 CHAPTER โ… . Northward expansion of water deer (Hydropotes inermis) the potential prey for big cats: origin and distribution 22 Introduction 22 Material and methods 24 Result 31 Discussion 45 CHAPTER โ…ก. Prediction of water deer habitat and potential habitat patches connection 53 Introduction 54 Material and methods 59 Result 68 Discussion 80 CHAPTER โ…ข. Local people attitude towards big cats and their prey 86 Introduction 87 Material and methods 90 Result 95 Discussion 116 Appendix 122 General Discussion 142 Bibliography 146 Abstract in Korean 162๋ฐ•

    Riemannian statistical techniques with applications in fMRI

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    Over the past 30 years functional magnetic resonance imaging (fMRI) has become a fundamental tool in cognitive neuroimaging studies. In particular, the emergence of restingstate fMRI has gained popularity in determining biomarkers of mental health disorders (Woodward & Cascio, 2015). Resting-state fMRI can be analysed using the functional connectivity matrix, an object that encodes the temporal correlation of blood activity within the brain. Functional connectivity matrices are symmetric positive definite (SPD) matrices, but common analysis methods either reduce the functional connectivity matrices to summary statistics or fail to account for the positive definite criteria. However, through the lens of Riemannian geometry functional connectivity matrices have an intrinsic non-linear shape that respects the positive definite criteria (the affine-invariant geometry (Pennec, Fillard, & Ayache, 2006)). With methods from Riemannian geometric statistics, we can begin to explore the shape of the functional brain to understand this non-linear structure and reduce data-loss in our analyses. This thesis oโ†ตers two novel methodological developments to the field of Riemannian geometric statistics inspired by methods used in fMRI research. First we propose geometric- MDMR, a generalisation of multivariate distance matrix regression (MDMR) (McArdle & Anderson, 2001) to Riemannian manifolds. Our second development is Riemannian partial least squares (R-PLS), the generalisation of the predictive modelling technique partial least squares (PLS) (H. Wold, 1975) to Riemannian manifolds. R-PLS extends geodesic regression (Fletcher, 2013) to manifold-valued response and predictor variables, similar to how PLS extends multiple linear regression. We also generalise the NIPALS algorithm to Riemannian manifolds and suggest a tangent space approximation as a proposed method to fit R-PLS. In addition to our methodological developments, this thesis oโ†ตers three more contributions to the literature. Firstly, we develop a novel simulation procedure to simulate realistic functional connectivity matrices through a combination of bootstrapping and the Wishart distribution. Second, we propose the R2S statistic for measuring subspace similarity using the theory of principal angles between subspaces. Finally, we propose an extension of the VIP statistic from PLS (S. Wold, Johansson, & Cocchi, 1993) to describe the relationship between individual predictors and response variables when predicting a multivariate response with PLS. All methods in this thesis are applied to two fMRI datasets: the COBRE dataset relating to schizophrenia, and the ABIDE dataset relating to Autism Spectrum Disorder (ASD). We show that geometric-MDMR can detect group-based diโ†ตerences between ASD and neurotypical controls (NTC), unlike its Euclidean counterparts. We also demonstrate the efficacy of R-PLS through the detection of functional connections related to schizophrenia and ASD. These results are encouraging for the role of Riemannian geometric statistics in the future of neuroscientific research.Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 202

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospitalโ€™s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    The Impact of Dietary Macronutrient Composition on Noncommunicable Diseases and Aging: A Life Course Approach

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    Background: Nutrition is vital for human health and is a key modifiable risk factor in the development of noncommunicable diseases (NCDs), which account for 74% of global annual deaths. Aging increases disease risk, and nutritional associations vary across life stages. However, current research often focuses on individual nutrients rather than complex associations. Therefore, this thesis investigates the association of macronutrients with NCDs and aging throughout the lifespan using a multi-nutrient approach known as the Geometric Framework for Nutrition. Aims: Six studies were conducted to explore the following aims at varying stages of the life course: 1) How are macronutrients linked to NCDs? 2) Is dietary macronutrient composition associated with markers of biological aging? 3) What roles do diet quality, food groups, and factors like the microbiome play in macronutrient-NCD and aging relationships? Results: Findings from this thesis revealed a complex nonlinear relationship for macronutrients with aging (Chapters 4, 6), metabolic health (Chapters 4, 5), and disease outcomes (Chapter 7). These relationships suggest that there is no single optimal macronutrient composition for all outcomes. Notably, the microbiome was shown to play a potential effect-modifying role in how diet impacts cardiometabolic health (Chapter 8). Furthermore, the final study revealed that macronutrient composition associations with NCDs widely differ according to diet quality (Chapter 9). Conclusions: Dietary macronutrient composition is intricately linked to metabolic health, aging, and NCD risk, with variations based on diet quality, life stage, and potential modification by factors like the microbiome. The findings emphasize the need for a comprehensive and standardized approach to nutritional research that considers each of these aspects before providing dietary guidance or making public health recommendations

    Learning representations for graph-structured socio-technical systems

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    The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism. The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased emphasis on addressing the limitations of interpretability in graph representation learning. This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations

    A framework to measure the robustness of programs in the unpredictable environment

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    Due to the diffusion of IoT, modern software systems are often thought to control and coordinate smart devices in order to manage assets and resources, and to guarantee efficient behaviours. For this class of systems, which interact extensively with humans and with their environment, it is thus crucial to guarantee their correct behaviour in order to avoid unexpected and possibly dangerous situations. In this paper we will present a framework that allows us to measure the robustness of systems. This is the ability of a program to tolerate changes in the environmental conditions and preserving the original behaviour. In the proposed framework, the interaction of a program with its environment is represented as a sequence of random variables describing how both evolve in time. For this reason, the considered measures will be defined among probability distributions of observed data. The proposed framework will be then used to define the notions of adaptability and reliability. The former indicates the ability of a program to absorb perturbation on environmental conditions after a given amount of time. The latter expresses the ability of a program to maintain its intended behaviour (up-to some reasonable tolerance) despite the presence of perturbations in the environment. Moreover, an algorithm, based on statistical inference, it proposed to evaluate the proposed metric and the aforementioned properties. Throughout the paper, two case studies are used to the describe and evaluate the proposed approach
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