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 Terrestrial biomes, defined by unique climatic conditions and evolutionary histories, are increasingly vulnerable to global climate change. This study evaluates the exposure of Earth's 14 terrestrial biomes to climate change by characterizing their climatic boundaries using n ‐dimensional probabilistic methods. We analyzed precipitation and temperature data from the Global 200 project, combined with bioclimatic variables and climate models, to assess changes across historical, present, and future scenarios (Shared Socioeconomic Pathways: SSP1‐2.6, SSP3‐7.0, and SSP5‐8.5). Using NicheROVER and NicheA, we quantified climatic overlap and projected non‐overlapping regions under future climate scenarios. Our results reveal significant climatic specificity in temperature and precipitation ranges, with Flooded Grasslands & Savannas, Tropical & Subtropical Dry Broadleaf Forests, and Mangroves identified as the most exposed biomes. Geographic projections for 2040 highlight high‐exposure regions near the equator, including the Neotropics, Central and Northern Africa, Southern Asia, Oceania, and Antarctica. These regions intersect with 2230 and 9091 protected areas under the SSP3‐7.0 and SSP5‐8.5 scenarios, respectively. This study underscores the importance of characterizing climatic boundaries to enhance ecosystem resilience estimates and inform biodiversity conservation strategies. By integrating niche modeling approaches, we provide a framework for identifying regions most vulnerable to climate change. Our findings emphasize the need for targeted conservation efforts, policy interventions, and fine‐scale analyses to mitigate climate impacts on biodiversity, agriculture, and human health. We conclude that understanding biome‐specific climatic boundaries is critical for addressing the global climate crisis and guiding adaptive management in the most exposed regions.
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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