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
The prevalence of a species in a given area is crucial for estimating the environmental conditions associated with its subsistence within ecological niche models (ENMs). Prevalence is defined as the proportion of presences relative to the total number of sampled sites, reflecting prior expectation on species commonness or rarity. However, reliable estimation often faces challenges due to limited or biased occurrence data, particularly for rare or poorly monitored species. This work presents a data-driven, multi-species methodology to estimate species prevalence for use in ENMs. It leverages species occurrence records from the Global Biodiversity Information Facility and is entirely unsupervised. It utilises two clustering methods, one deep-learning model, and an ensemble model, plus statistical analysis to classify species commonness and transform classifications into prevalence probabilities. A case study is presented for 161 species living in the Massaciuccoli Lake basin (Tuscany, Italy), a wetland of high biodiversity value and ecological sensitivity. The models classified the species' prevalence based on observations from other Italian wetland sites, and were evaluated against expert-based assessments. All models achieved high accuracy, with the deep-learning model achieving the highest (~ 81-90%). The proposed methodology is scalable and reproducible and can inform ENMs with objective, robust prevalence estimates.
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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