Recruitment facilitation can drive alternative states on temperate reefs
Bibliographic record
Abstract
How the combination of positive and negative species interactions acts to drive community dynamics is a fundamental question in ecology. Here we explore one aspect of this question by expanding the theory of predator-mediated coexistence to include the potential role of facilitation between the predator and inferior competitor. To motivate and illustrate our simple model, we focus on sea-urchin-algae interactions in temperate rocky reef systems and incorporate recruitment facilitation, a common characteristic of marine systems. Specifically, the model represents sea urchin grazing on macroalgae, macroalgal competition with crustose coralline algae (CCA), and facilitation of sea urchin recruitment to CCA. These interactions generate alternative stable states, one dominated by macroalgae and the other by urchins, which do not occur when recruitment facilitation of urchins to CCA is ignored. Therefore, recruitment facilitation provides a possible mechanism for alternative kelp forest and urchin barren states in temperate marine systems, where storm events or harvesting of urchins or their predators can drive switches between states that are difficult to reverse. In systems with such dynamics, spatial management such as no-take marine reserves may play a crucial role in protecting community structure by increasing the resilience to shifts between states.
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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) | 0.005 | 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 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".