Food Webs: Insights from a General Ecosystem Model
Bibliographic record
Abstract
Abstract Food webs have been intensively studied throughout modern ecology, using empirical evidence, statistics and modelling tools to search for consistent patterns, underlying commonalities, and variations between food webs and ecosystems. However, with few exceptions, the modelling approaches have not been based on the emergent properties of complex simulated ecosystems. For the same reason, there have also been few studies of ‘sampling the model’, in which different levels of sampling effort are imposed in a controlled simulated environment to explore the effects of varying sampling intensity in order to relate those back to empirical observations. Here, we introduce the Madingley Model, a general ecosystem model based on ecological and biological first principles of interactions between individuals, as a potential tool for analyzing food webs. In doing so, we present the first insights of the analyses of emergent Madingley food web networks. We describe the basic structure of these networks and introduce a process to aggregate and sample the food webs produced by this model so that it can be compared to empirical food web studies. We show that food webs created by this model reasonably reproduce the properties of empirical food web networks. Furthermore, we provide insights about the effects of species aggregation and sampling on the observed structure of empirically documented food webs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".