Energetic constraints to food chain length in a metacommunity framework
Bibliographic record
Abstract
As metabolism increases with body size, populations of large-sized species can become constrained to relatively low trophic positions within a local community due to their high energetic demands combined with the limited efficiency with which energy is transferred up the food chain from primary producers. In a metacommunity context, dispersal can become a major driver of population dynamics and persistence, having also the potential to ameliorate the aforementioned energetic constraints due to its effect on colonization and energy subsidies from connected patches. Here, we derive a size-dependent model for food chain length in communities subjected to migration and show that populations with higher influx of migrants are able to persist at higher trophic positions. Simulations of random, dendritic, and real aquatic metacommunities corroborate this result and further indicate that community location in the landscape (relative centrality or isolation) may determine local food web structure. More central patches contained larger populations and were less constrained in trophic position. This effect was particularly strong in dendritic metacommunities, which are representative of freshwater watersheds in general. Furthermore, the role of spatial processes is nonlinearly intensified with increases in body size, indicating that larger organisms have a much stronger dependence on landscape attributes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".