인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Background Predicting the potential habitat of Phytolacca americana, a high-risk invasive species, can help provide a scientific basis for its quarantine and control strategies. Using the optimized MaxEnt model, we applied the latest climate data, CMIP6, to predict the distribution of potential risk zones and their change patterns for P . americana under current and future (SSP126, SSP245, SSP585) climate conditions, followed by invasion potential analysis. Results The predictions of MaxEnt model based on R language optimization were highly accurate. A significantly high area of 0.8703 was observed for working characteristic curve (AUC value) of subject and the kappa value was 0.8074. Under the current climate conditions, the risk zones for P. americana were mainly distributed in Sichuan, Chongqing, Guizhou, Hunan, and Guangxi provinces. The contribution rate of each climatic factor of P . americana was calculated using the jackknife test. The four factors with the highest contribution rate included minimum temperature of coldest month (bio6, 51.4%), the monthly mean diurnal temperature difference (bio2, 27.9%), precipitation of the driest quarter (bio17, 4.9%), and the warmest seasonal precipitation (bio12, 4.3%). Conclusion Under future climatic conditions, the change in the habitat pattern of P . americana generally showed a migration toward the Yangtze River Delta region and the southeastern coastal region of China. This migration exhibited an expansion trend, highlighting the strong future invasiveness of the species. Based on the predictions, targeted prevention and control strategies for areas with significant changes in P . americana were developed. Therefore, this study emphasizes the need of an integrated approach to effectively prevent the further spread of invasive plants.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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