ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2023
A framework for the detection and attribution of biodiversity change.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed.
- The utility of herbarium collections for genetic monitoring.Bioscience · 2026Review
- Predicting Coral Cover Trends From Local to Broad Spatial Scales.Global change biology · 2026Article
- Article
- BON in a Box: An Open and Collaborative Platform for Biodiversity Monitoring, Indicator Calculation, and Reporting.Bioscience · 2026Article
- From data to decisions: Toward a Biodiversity Monitoring Standards Framework.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Aiming Higher to Test a Bend in the Curve of Biodiversity Loss: The Challenge of Halt-The-Loss Targets.Ecology and evolution · 2026Article
- A digital twin for real-time biodiversity forecasting with citizen science data.Nature ecology & evolution · 2026Article
- Eelgrass ecosystem collapse and social-ecological regime shift driven by hydropower development and climate change.Nature communications · 2026Article
- Persistent loss of animal diversity on a rocky shore over nine decades and across multiple investigators.PeerJ · 2026Article
- Article
- Biodiversity science and biosurveillance are fellow travelers.Bioscience · 2025Article
- Invasion timing affects multiple scales, metrics, and facets of biodiversity outcomes in ecological restoration experiments.Ecological applications : a publication of the Ecological Society of America · 2025Article
- Linking energetic instability to compositional changes in biological communities.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Monitor social-ecological systems to achieve global goals for biodiversity and nature's contributions to people.Bioscience · 2025Review
- A Method for Conveying Confidence in iNaturalist Observations: A Case Study Using Non-Native Marine Species.Ecology and evolution · 2024Article
- Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data.eLife · 2024Article
- National commitments to Aichi Targets and their implications for monitoring the Kunming-Montreal Global Biodiversity Framework.npj biodiversity · 2024Article
- Article
- Synthesis reveals approximately balanced biotic differentiation and homogenization.Science advances · 2024Article
- Global monitoring for biodiversity: Uncertainty, risk, and power analyses to support trend change detection.Science advances · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The causes of biodiversity change are of great scientific interest and central to policy efforts aimed at meeting biodiversity targets. Changes in species diversity and high rates of compositional turnover have been reported worldwide. In many cases, trends in biodiversity are detected, but these trends are rarely causally attributed to possible drivers. A formal framework and guidelines for the detection and attribution of biodiversity 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 biodiversity change in relation to hypothesized impacts of multiple potential drivers and can eliminate putative drivers from contention. The framework encourages a formal and reproducible statement of confidence about the role of drivers after robust methods for trend detection and attribution have been deployed. Confidence in trend attribution requires that data and analyses used in all steps of the framework follow best practices reducing uncertainty at each step. We illustrate these steps with examples. This framework could strengthen the bridge between biodiversity science and policy and support effective actions to halt biodiversity loss and the impacts this has on ecosystems. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.