Designing effective reserve networks for nonequilibrium metacommunities
Bibliographic record
Abstract
The proliferation of efficient fishing practices has promoted the depletion of commercial stocks around the world and caused significant collateral damage to marine habitats. Recent empirical studies have shown that marine reserves can play an important role in reversing these effects. Equilibrium metapopulation models predict that networks of marine reserves can provide similar benefits so long as individual reserves are sufficiently large to achieve self-sustainability, or spaced based on the extent of dispersal of the target species in order to maintain connectivity between neighboring reserves. However, these guidelines have not been tested in nonequilibrium metacommunity models that exhibit the kinds of complex spatiotemporal dynamics typically seen in natural marine communities. Here, we used a spatially explicit predator-prey model whose predictions have been validated in a marine system to show that current guidelines are not optimal for metacommunities. In equilibrium metacommunities, there is a community-level trade-off for designing effective reserves: Networks whose size and spacing are smaller than the extent of dispersal maximize global predator abundance but minimize global prey abundance because of trophic cascades, whereas the converse is true for reserve networks whose size and spacing are larger than the extent of dispersal. In nonequilibrium metacommunities, reserves whose size and spacing match the extent of spatial autocorrelation in adult abundance (i.e., the extent of patchiness) escape this community-level trade-off by maximizing global abundance and persistence of both the prey and the predator. Overall, these results suggest that using the extent of adult patchiness instead of the extent of larval dispersal as the size and spacing of reserve networks is critical for designing community-based management strategies. By emphasizing patchiness over dispersal distance, our results show how the apparent complexity of nonequilibrium communities can actually simplify management guidelines and reduce uncertainty associated with the assessment of dispersal in marine environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".