Habitat coupling dynamics of mobile consumers along a freshwater and marine resource gradient in a sub-Arctic estuarine system
Bibliographic record
Abstract
Food webs consist of numerous connections between consumers and resources that can couple adjacent ecosystems together by the movement of nutrients, prey, and consumers leading to habitat coupling. Habitat coupling and isotopic niche dynamics among mobile consumers at the individual and population-level has been examined extensively in freshwater systems but has received little attention in sub-Arctic estuaries, which act as transition zones between freshwater and marine habitats and resources. The objective of this study was to quantify the diet composition between freshwater-and marine-derived resources and the isotopic niche size of mobile consumers (13 fishes and 2 seal species) within the lower Churchill River, Manitoba, Canada using stable isotopes (δ13C, δ15N and δ34S) in a Bayesian framework. Cisco, lake whitefish, and northern pike represented the habitat couplers in this system and also exhibited the greatest amount of individual variability since most (75%, 56%, 65% of individuals for cisco, lake whitefish, and pike respectively) consumed a mix of both freshwater-and marine-derived resources. The largest isotopic niche sizes were found for lake whitefish and northern pike, whereas the smallest isotopic niche sizes were found for both harbour and ringed seals. The isotopic niche of lake whitefish overlapped with the most species (5 in total), which supports their broader use of both freshwater- and marine-derived resources. Habitat couplers in this system exhibited more variability in their foraging strategy than the other consumers, which aligns with their known life histories and migratory or resident movement strategies. Future changes in the relative availability of freshwater and marine-derived resources due to climate and anthropogenic stressors could pose consequences to these habitat coupling species and overall trophic dynamics of sub-Arctic estuarine systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".