Demographic trade‐offs in a neutral model explain death‐rate–abundance‐rank relationship
Bibliographic record
Abstract
The neutral theory of biodiversity has been criticized for its neglect of species differences. Yet it is much less heeded that S. P. Hubbell's definition of neutrality allows species to differ in their birth and death rates as long as they have an equal per capita fitness. Using the lottery model of competition we find that fitness equalization through birth-death trade-offs can make species coexist longer than expected for demographically identical species, whereas the probability of monodominance for a species under zero-sum neutral dynamics is equal to its initial relative abundance. Furthermore, if newly arising species in a community survive preferentially they are more likely to slip through the quagmire of rareness, thus creating a strong selective bias favoring their community membership. On the other hand, high-mortality species, once having gained a footing in the community, are more likely to become abundant due to their compensatory high birth rates. This unexpected result explains why a positive association between species abundance and per capita death rate can be seen in tropical-forest communities. An explicit incorporation of interspecific trade-offs between birth and death into the neutral theory increases the theory's realism as well as its predictive power.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".