Habitat and Trophic Specialization Among Greenland Cod (<i>Gadus ogac</i>) Morphotypes in the Context of Climate Change Resilience
Bibliographic record
Abstract
ABSTRACT Morphological variation is observed in many fish species; however, the direct ecological consequences of this variation in terms of specialists or generalists in resource use are rarely studied. Understanding the degree of specialist or generalist behavior among morphotypes has the potential to provide insight into the ability of Arctic fish species to adapt to ongoing climate change. Here, we estimated morphological variation and habitat‐trophic metrics from carbon (δ13C) and nitrogen (δ15N) stable isotopes in Greenland cod (Gadus ogac) collected along the marine coast near Ulukhaktok, Northwest Territories (NT), in the western Canadian Arctic (n = 45). Principal component analysis (PCA) of linear morphometric measurements and subsequent k‐means clustering categorized fish into two morphological groups driven primarily by head shape and body depth. Mean δ13C and δ15N values did not differ significantly between morphological groups; however, measures of individual specialization showed that the morphotype with the smaller head and slender body had lower habitat specialization and higher trophic specialization compared to the morphotype with the larger head and stockier body. This observed gradient suggests that morphotype‐specific behaviors can be observed over a generalist‐specialist gradient rather than as distinct groups and may benefit generalist populations in the future due to their ability to undergo resource shifts. The integrated approach used here informs our understanding of species' flexibility to competition and food web shifts with ongoing borealization. The findings highlight the importance of considering individual‐level data and the degree to which a population exhibits specialization‐generalization in fisheries co‐management in the Arctic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".