Trait‐dependency of trophic interactions in zooplankton food webs
Bibliographic record
Abstract
Anthropogenic change in the abundance or identity of dominant top predators may induce reorganizations in whole food webs. Predicting these reorganizations requires identifying the biological rules that govern trophic niches. However, we still lack a detailed understanding of the respective contributions of body size, behaviour (e.g. match between predator hunting mode and prey antipredator strategy), phylogeny and/or ontogeny in determining both the presence and strength of trophic interactions. Here, we address this question by measuring zooplankton numerical response to fish predators in lake enclosures. We compared the fit to zooplankton count data of models grouping zooplankters based either on 1) body sizes, 2) antipredator behaviour, 3) body size combined with antipredator behaviour or on 4) phylogeny combined with ontogeny (i.e. different life stages of copepods). Body size was a better predictor of zooplankton numerical response to fish than antipredator behaviour, but combining body size and behaviour provided even better predictions. Models based on phylogeny combined with ontogeny clearly outperformed those based on other zooplankton grouping rules, except when phylogeny was poorly resolved. Removing ontogenetic information plagued the predictive power of the highly‐resolved (genus‐level) phylogenetic grouping but not of medium‐resolved or poorly‐resolved phylogenetic grouping. Our results support the recent use of phylogeny as a superior surrogate for traits controlling trophic niches, and further highlight the added value of combining phylogeny with ontogenetic traits. Further improvements in our mechanistic understanding of how trophic networks are shaped are bound to uncovering the trophic traits captured by phylogeny and ontogeny, but that currently remain hidden to us.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".