Patterns of lake occupancy by fish indicate different adaptations to life in a harsh Arctic environment
Bibliographic record
Abstract
Summary For six fish species sampled from 86 lakes on the Arctic Coastal Plain, Alaska, we examined whether lake occupancy was related to variables representing lake size, colonisation potential and/or the presence of overwintering habitat. We found the relative importance of each factor for a given species could be related to its ecology and adult size. The three large‐bodied migratory species, least cisco ( Coregonus sardinella ), broad whitefish ( Coregonus nasus ) and arctic grayling ( Thymallus arcticus ), were influenced by factors associated with the likelihood of fish recolonising lakes, including whether the lakes had a stream connection. Of the large‐bodied species, least cisco had the highest likelihood of occupancy (0.52 ± 0.05) and models provided evidence that least cisco exhibit both migratory and resident forms. Models for small‐bodied fish differed among species, indicating different niches. Ninespine stickleback ( Pungitius pungitius ) were the most widespread and ubiquitous of the species captured (occupancy probability = 0.97 ± 0.01); they were captured in lakes that freeze to the bottom, suggesting that they disperse widely and rapidly after the spring freshet, including colonisation of sink habitats. Alaska blackfish ( Dallia pectoralis ) had a lower occupancy (occupancy probability = 0.76 ± 0.05) with a distribution that reflected tolerance to harsh conditions. Slimy sculpin ( Cottus cognatus ) had an occupancy probability of 0.23 ± 0.06, with a distribution indicating its marine origin. Based on these patterns, we propose an overall model of primary controls on the distribution of fish on the Arctic Coastal Plain of Alaska. Harsh conditions, including lake freezing, limit occupancy in winter through extinction events while lake occupancy in spring and summer is driven by directional migration (large‐bodied species) and undirected dispersal (small‐bodied species).
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".