Patch Dynamics, Persistence, and Species Coexistence in Metaecosystems
Bibliographic record
Abstract
We add an ecosystem perspective to spatially structured communities subject to colonization-extinction dynamics. We derive a plant-based metaecosystem model to analyze how the spatial flows of the biotic and abiotic forms of a limiting nutrient affect persistence and coexistence. We show that the proportion of patches supporting plants in a region has a considerable impact on local nutrient dynamics. Then we explicitly couple nutrient dynamics to patch dynamics. Our model shows strong feedback between local and regional dynamics mediated by nutrient flows. We find that nutrient flows can have either positive or negative effects on species persistence and coexistence. The essential feature of this local-regional coupling is the net direction of the nutrient flows between occupied and empty patches. A net flow of nutrients from occupied to empty patches leads to indirect facilitative interactions, such as an inferior competitor promoting the persistence of a superior competitor. We show that nutrient flows affect the potential diversity of the metaecosystem and key features of plant community dynamics, such as the shape of the competition-colonization trade-off and successional sequences. Our analysis revealed that integrating ecosystem and spatial dynamics can lead to various indirect interactions that contribute significantly to community organization.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".