Reciprocal feedbacks between spatial subsidies and reserve networks in coral reef meta‐ecosystems
Bibliographic record
Abstract
Top-down processes such as predation and herbivory have been shown to control the dynamics of communities across a range of ecosystems by generating trophic cascades. However, theory is only beginning to describe how these local trophic processes interact with spatial subsidies in the form of material (nutrient, detritus) transport and organismal dispersal to (1) shape the structure of interconnected (meta-) ecosystems and (2) determine their optimal management via reserve networks. Here, we develop a meta-ecosystem model to understand how the reciprocal feedbacks between spatial subsidies and reserve networks modulate the importance of top-down control in a simple herbivorous fish-macroalgae-coral system. We show that in large and isolated reserve networks where connectivity between protected and unprotected areas is limited, spatial subsidies remain largely confined to reserves. This retention of spatial subsidies promotes the top-down control of corals and macroalgae by herbivores inside reserves but reduces it outside reserves. Conversely, in small and aggregated reserves where connectivity between protected and unprotected areas is high, the spillover of spatial subsidies causes a reduction in top-down control of corals and macroalgae by herbivores inside reserves and an increase in the strength of top-down control outside reserves. In addition, we demonstrate that there is a trade-off between local and regional conservation objectives when designing reserve networks: small and aggregated reserves based on the extent of dispersal maximize the abundance of corals and herbivores regionally, whereas large and isolated reserves always maximize the abundance of corals within reserves, regardless of the extent of dispersal. The existence of such "conservation traps," which arise from the fulfillment of population-level objectives within local reserves at the cost of community-level objectives at regional scales, suggests the importance of adopting a more holistic strategy to manage complex and interconnected ecosystems.
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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 | 0.001 |
| 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 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".