Estimates of local biodiversity change over time stand up to scrutiny
Bibliographic record
Abstract
Abstract Two recent meta-analyses of local-scale biodiversity change over time, by the authors of the present paper, have been subject to a harsh critique. Here we use new data and analyses to respond to the main points of this critique. First, a central argument of the critique was that short-term time series lead to biased estimates of long-term biodiversity change. However, we show here that this conclusion was based entirely on two fundamental mistakes in the simulations used to support it. Second, we show that the critic's conclusion that there are negative relationships between temporal biodiversity change and study duration is entirely dependent on: (i) the unrealistic assumption that biodiversity change can be positive when study duration = 0; (ii) the use of only a subset of the available data; (iii) inclusion of a single outlier data point in a single study (out of 100 in this case); and/or (iv) a choice to use log ratios rather than slopes (when available) as the metric of temporal biodiversity change. In short, the evidence does not support the conclusion that studies of longer duration tend to find local diversity decline. Finally, the critique highlighted the obviously true fact that studies in the ecological literature represent a geographically biased sample of locations on Earth; this issue was noted in both original papers, and is relevant for all ecological data syntheses. This fact was used by the critics to cast doubt on our conclusion that, outside of areas converted to croplands or asphalt, the distribution of temporal biodiversity trends is centered on zero. As a scientific rule, future studies based on more or different data may cause us to modify our conclusion, but at present, alternative conclusions based on the geographic-bias argument rely entirely on speculation. In sum, the critique raises points of uncertainty typical of all ecological studies, but it falls far short of providing an evidence-based alternative interpretation for our results, which are now supported by syntheses of hundreds of individual data sets of temporal biodiversity change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.241 | 0.497 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".