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原文連結
論文資訊
- 類型：已發表論文
- 日期：2023-05-29
摘要
The causes of 生物多樣性 change are of great scientific interest and central to policy efforts aimed at meeting 生物多樣性 targets. Changes in 物種 多樣性 and high rates of compositional turnover have been reported worldwide. In many cases, trends in 生物多樣性 are detected, but these trends are rarely causally attributed to possible drivers. A formal framework and guidelines for the detection and attribution of 生物多樣性 change is needed. We propose an inferential framework to guide detection and attribution analyses, which identifies five steps-causal modelling, observation, estimation, detection and attribution-for robust attribution. This workflow provides evidence of 生物多樣性 change in relation to hypothesized impacts of multiple potential drivers and can eliminate putative drivers from contention. The framewor
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