Why body size matters: how larger fish ontogeny shapes ecological network topology
Bibliographic record
Abstract
Ontogenetic development can strongly shape species interactions. Yet, rarely is stage‐structure considered when analyzing species interaction networks, particularly networks that can account for more than feeding relationships. Here, we assess 1) if body size or trophic level regulate the importance of species' ontogeny on their interactions and 2) how including relevant stage‐structure affects the topology of species interaction networks. We use a count‐based inferential method to create networks from adult and juvenile fish count data and test stage‐structure importance by comparing a model that includes stage‐structure for all species against models that include stage‐structure only for larger fishes and only for piscivorous fishes during network construction. While the inferential method we use cannot differentiate between different types of interactions, it can account for different interaction types within a network as a pairwise interaction is inferred when one species influences the abundance of another. Next, we use graphlet‐based techniques to test if including stage‐structure alters overall network topology and a linear model to measure if adult‐juvenile size differences drive interaction differences at a species‐level. We find that the model that includes stage‐structure only for larger fishes outperforms other stage‐structured models including the model with only piscivore stage‐structure, and that larger differences in body size among juveniles and adults lead to greater interaction dissimilarities. Moreover, we find topological differences between inferred networks that only include adults and those that account for the stage‐structure of larger species. Overall, our study demonstrates how stage‐structured topological changes can be measured using inferred interaction networks and illustrates how larger species' juveniles fundamentally shape the structure of stream fish communities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".