Deleting species from model food webs
Bibliographic record
Abstract
In natural biological communities the disappearance of one species can have knock‐on effects causing extinction of further species from the food web. To investigate these effects we used an evolutionary model to assemble many independent simulated food webs, and studied their dynamical behaviour when one species was deleted. On average, only 2.1% of the remaining species went extinct as a result of the deletion. However, the probability of extinction of predators and indirect predators (more than one link up the chain) of the deleted species was several times larger than for an average species. The model allows predators to adapt their choice of prey in response to changing frequencies of the prey. It was found that the larger the proportion of the deleted species in the predator's diet, the greater its probability of extinction. The probability of extinction of prey of the deleted species was also significantly higher than for an average species. This is due to increased competition between prey species after removal of their predator. The effect was largest for prey species that formed an intermediate fraction of the diet of the deleted species. The number of further extinctions increased significantly with the number of links in the food web to the deleted species prior to deletion, and was also correlated with the bottom‐up and top‐down keystone species indices. We also considered which properties of the web as a whole influenced its robustness to species deletion. This revealed a significant correlation between ecosystem redundancy and deletion stability, but no clear relationship with complexity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".