Estimating the feeding range of a mobile consumer in a river-flood plain system using δ13C gradients and parasites
Bibliographic record
Abstract
1. The feeding range of an individual is central to food web dynamics as it determines the spatial scale of predator-prey interactions. However, despite recognition of its importance as a driving force in population dynamics, establishing feeding range is seldom done as detailed information on trophic interactions is difficult to obtain. 2. Biological markers are useful to answer this challenge as long as spatial heterogeneity in signal is present within the area investigated. A spatially complex ecosystem, Lake St. Pierre (LSP), a fluvial lake of the St Lawrence River (Québec, Canada), offered a unique opportunity to determine the feeding range of a secondary consumer, yellow perch (Perca flavescens) using isotopic ratios of carbon (δ(13)C). However, because food chains based on phytoplankton have generally more negative δ(13) C than those depending on periphyton, it was essential to determine the contribution of zooplankton in fish diet to correctly interpret spatial patterns of δ(13)C. We used parasites in perch to examine whether their δ(13)C was reflecting local δ(13)C baseline conditions rather than a feeding specialization on zooplankton. 3. δ(13)C of primary consumers was highly variable and exhibited a striking gradient along the shore-channel axis, suggesting that δ(13)C should reflect an individual consumer's spatial position in LSP. 4. This strong isotopic gradient allowed us to estimate the spatial scale of the resources used by individual perch following an approach presented by Rasmussen, Trudeau & Morinville (Journal of Animal Ecology, 78, 2009, 674). By comparing the δ(13)C variability in perch to that of primary consumers, we estimated that the adults feeding range was around 2 km along the shore-channel axis. 5. The combined use of isotopic ratios and parasites allowed us to determine that the adult population uses a wide range of habitats between the flood plain and the main channel. However, individually, each perch depended on a limited foodshed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".