Can latitudinal richness gradients be measured in the terrestrial fossil record?
Bibliographic record
Abstract
Abstract Studying the deep-time origins of macroecological phenomena can help us to understand their long-term drivers. Given the considerable spatiotemporal bias of the terrestrial fossil record, it behooves us to understand how much biological information is lost. The aim of this study is to establish whether latitudinal diversity gradients are detectable in a biased terrestrial fossil record. I develop a simulated fossilization approach, weighting the probability of terrestrial mammal species appearing in the fossil record based on body size and geographic-range size; larger species with larger range sizes are more likely to enter the fossil record. I create simulated fossil localities from the modern North American mammal record. I vary the percentage of species successfully fossilized and estimate the magnitude of the latitudinal diversity gradient (slope of the richness gradient and degree of species turnover). I find that estimates of the latitudinal diversity gradient are sensitive to the loss of species with small body size and geographic-range sizes. In some cases, simulated fossil-record bias completely obliterates evidence of declining richness with latitude, a phenomenon that is not ameliorated by the application of nonparametric richness estimation. However, if the rate of preservation is medium (50% of species) to high (75% of species), the magnitude of the latitudinal diversity gradient can be reliably estimated. Similarly, changes in the diversity gradient estimates are largely explained by differences in the diversity–climate relationship among iterations, suggesting that these relationships may be measurable in the fossil record.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".