The Living Planet Index's ability to capture biodiversity change from uncertain data
Bibliographic record
Abstract
Code for simulations, calculations, and figures presented in the manuscript "The Living Planet Index's ability to capture biodiversity change from uncertain data" by Hébert, Katherine & Gravel, Dominique.<br> Abstract: The Living Planet Index (LPI) is a crucial tool to track global biodiversity change , but necessarily sacrifices information to summarize thousands of population trends into a single communicable index. Evaluating when and how this information loss affects the LPI’s performance is essential to ensure interpretations of the index reflect the truth as reliably as possible. Here, we evaluated the ability of the LPI to accurately and precisely capture trends of population change from uncertain data. We derived a mathematical analysis of uncertainty propagation in the LPI to track how measurement and process uncertainty may bias estimates of population growth rate trends, and to measure the overall uncertainty of the LPI. We demonstrated the propagation of uncertainty using simulated scenarios of declining, stable, or growing populations fluctuating independently, synchronously, or asynchronously, to assess the bias and uncertainty of the LPI in each scenario. We found that measurement and process uncertainty consistently pull the index below the expected true trend. Importantly, variability in the raw data scales up to draw the index further below the expected trend and to amplify its uncertainty, particularly when populations are small. These findings echo suggestions that a more complete assessment of the variability in population change trends, with particular attention to covarying populations, would enrich the LPI’s already critical influence on conservation communication and decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.455 | 0.319 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".