Let's go beyond taxonomy in diet description: testing a trait‐based approach to prey–predator relationships
Bibliographic record
Abstract
Understanding 'Why a prey is a prey for a given predator?' can be facilitated through trait-based approaches that identify linkages between prey and predator morphological and ecological characteristics and highlight key functions involved in prey selection. Enhanced understanding of the functional relationships between predators and their prey is now essential to go beyond the traditional taxonomic framework of dietary studies and to improve our knowledge of ecosystem functioning for wildlife conservation and management. We test the relevance of a three-matrix approach in foraging ecology among a marine mammal community in the northeast Atlantic to identify the key functional traits shaping prey selection processes regardless of the taxonomy of both the predators and prey. Our study reveals that prey found in the diet of marine mammals possess functional traits which are directly and significantly linked to predator characteristics, allowing the establishment of a functional typology of marine mammal-prey relationships. We found prey selection of marine mammals was primarily shaped by physiological and morphological traits of both predators and prey, confirming that energetic costs of foraging strategies and muscular performance are major drivers of prey selection in marine mammals. We demonstrate that trait-based approaches can provide a new definition of the resource needs of predators. This framework can be used to anticipate bottom-up effects on marine predator population dynamics and to identify predators which are sensitive to the loss of key prey functional traits when prey availability is reduced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".