Quantifying the relevance of alternative stable states in many-species food webs
Bibliographic record
Abstract
Abstract Alternative stable ecosystem states are possible under the same environmental conditions in many models of 2-3 interacting species and an array of feedback loops. However, multi-species food webs might dissipate the feedbacks that create alternative stable states through species-specific traits and feedbacks. To test this potential, we develop a manyspecies model of consumer-resource interactions with two classes of feedbacks: specialized feedbacks where individual resources become unpalatable at high abundance, or aggregate feedbacks where overall resource abundance reduces consumer recruitment. We quantify how trophic interconnectedness and species differences in demography affect the potential for either feedback to produce alternative stable states dominated by consumers or resources. We find that alternative stable states are likely to happen in many-species food webs when aggregate feedbacks or lower species differences increase redundancy in species contributions to persistence of the consumer guild. Conversely, specialized palatability feedbacks with distinctive species roles in consumer guild persistence reduce the potential for alternative states but increase the likelihood that losing vulnerable consumers cascades into a food web collapse at low stress levels, a dynamic absent in few-species models. Altogether, among-species trait variation can limit the set of processes that create alternative stable states and impede consumer recovery from disturbance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".