Recasting spatial food web ecology as an ecosystem science
Bibliographic record
Abstract
Abstract Background/questions/methods Food webs are complex systems in which organisms interact with each other and with the abiotic aspects of their environment, thus acting as the conduit for transfers of energy and nutrients through ecosystems. Classical approaches to food webs focus strongly on patterns and processes occurring at the community level rather than at the broader ecosystem scale. Recent developments in community ecology suggest that spatial processes may be important in affecting food web dynamics and affect ecosystems as well, thus leading to the idea of meta-ecosystems. Here, we make a synthesis on how the links between food web dynamics and spatial ecosystem dynamics may be studied through (i) identifying differences between metacommunity and landscape ecology approaches when dealing with food webs, (ii) arguing that a tighter synthesis of the two approaches is needed for a good understanding of how diversity, ecosystem process and trait distributions in landscapes are related, and (iii) laying out how this gap can be efficiently bridged under the framework of meta-ecosystems. Results/conclusions We identify two possible sets of processes that drive spatial food webs and the ecosystems they occur in: trait-dependent processes and material-dependent processes. Both of these have been shown to be important in affecting various aspects of food web ecology and we ask how they may compare to each other and how they may interact. We argue that interactions between them, while complex, are likely and depend strongly on the size of the meta-ecosystem and its connectivity. A more integrative framework to the study of spatial food webs, which takes into account both approaches, might be key in better understanding the links between ecosystem and community dynamics at large 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".