Traits affecting nutrient recycling by mobile consumers can explain coexistence and spatially heterogeneous trophic regulation across a meta‐ecosystem
Bibliographic record
Abstract
Ecosystems are linked through spatial flows of organisms and nutrients that impact their biodiversity and regulation. Theory has predominantly studied passive nutrient flows that occur independently of organism movement. Mobile organisms, however, commonly drive nutrient flows across ecosystems through nutrient recycling. Using a meta-ecosystem model where consumers move between ecosystems, we study how consumer recycling and traits related to feeding and sheltering preferences affect species diversity and trophic regulation. We show local effects of recycling can cascade across space, yielding spatially heterogeneous top-down and bottom-up effects. Consumer traits impact the direction and magnitude of these effects by enabling recycling to favour a single ecosystem. Recycling further modifies outcomes of competition between consumer species by creating a positive feedback on the production of one competitor. Our findings suggest spatial interactions between feeding and recycling activities of organisms are key to predicting biodiversity and ecosystem functioning across spatial scales.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".