Population niche breadth and individual trophic specialisation of fish along a climate-productivity gradient
Bibliographic record
Abstract
Abstract A mechanistic understanding of how environmental change affects trophic ecology of fish at the individual and population level remains elusive. To address this, we conducted a space-for-time approach incorporating environmental gradients (temperature, precipitation and nutrients), lake morphometry (visibility, depth and area), fish communities (richness, competition and predation), prey availability (richness and density) and feeding (population niche breadth and individual trophic specialisation) for 15 native fish taxa belonging to different thermal guilds from 35 subarctic lakes along a marked climate-productivity gradient corresponding to future climate change predictions. We revealed significant and contrasting responses from two generalist species that are abundant and widely distributed in the region. The cold-water adapted European whitefish ( Coregonus lavaretus ) reduced individual specialisation in warmer and more productive lakes. Conversely, the cool-water adapted Eurasian perch ( Perca fluviatilis ) showed increased levels of individual specialism along climate-productivity gradient. Although whitefish and perch differed in the way they consumed prey along the climate-productivity gradient, they both switched from consumption of zooplankton in cooler, less productive lakes, to macrozoobenthos in warmer, more productive lakes. Species with specialist benthic or pelagic feeding did not show significant changes in trophic ecology along the gradient. We conclude that generalist consumers, such as warmer adapted perch, have clear advantages over colder and clear-water specialised species or morphs through their capacity to undergo reciprocal benthic–pelagic switches in feeding associated with environmental change. The capacity to show trophic flexibility in warmer and more productive lakes is likely a key trait for species dominance in future communities of high latitudes under climate change.
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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 | 0.001 | 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 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".