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 Aim Population dynamics are usually assessed through linear trend analysis, quantifying their general direction. However, linear trends may hide substantial variations in population dynamics that could reconcile apparent discrepancies when quantifying the extent of the biodiversity crisis. We seek to determine whether the use of non‐linear methods and the quantification of temporal variability can offer a more complete representation of changes in global population dynamics than commonly‐used linear approaches. Methods We analysed 6437 population time series from 1257 vertebrate species from the Living Planet Database over the period 1950–2020. We modelled populations through the use of second‐order polynomials and classified trajectories according to their direction and acceleration. We modelled and classified these same populations using a more classical linear trend analysis. We quantified temporal variability using the mean squared error of the fitted polynomials. We then used generalised linear mixed models to test potential sources of heterogeneity in non‐linear trajectories and temporal variability. Results In all, 44.8% of the analysed population time series were non‐linear. Across all populations, 30% were declining, 30% were increasing, and 40% were with no linear trend. Among the population showing no linear trend, half were concave or convex. Non‐linearity was expressed differently between taxonomic groups, with mammals showing higher prevalence of non‐linearity. Marine and freshwater populations were more variable than terrestrial populations, and fish were more variable than other vertebrates. Differences between geographical regions were detected in both non‐linearity and temporal variability, but no straightforward pattern emerged. There were no differences in both components between IUCN categories. Main Conclusions Non‐linearity and temporal variability reveal usually overlooked dramatic declines or recovery signals in global population dynamics. Thus, moving beyond linearity can improve our understanding of complex population dynamics and better inform conservation decisions. In particular, populations usually classified as ‘stable’ can hide informative changes in non‐linear and variability patterns that need to be considered in global biodiversity assessments.
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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