Dietary preferences of Ninespine Stickleback in high Arctic tundra streams
Bibliographic record
Abstract
Ninespine Stickleback Pungitius pungitius (Linnaeus, 1758) are generalist feeders of macroinvertebrates and zooplankton and are ubiquitous in Arctic freshwater and marine environments. Many invertivorous fish feed based on the abundance of different prey taxa, with this species known to select for abundant Baetidae and Daphniidae in Arctic ponds. Arctic stream invertebrate communities tend to be dominated by chironomids; however, these taxa may not hold the same nutritional value as others, such as Baetidae. Indeed, stickleback have previously shown selection for Baetidae in lentic environments, but little research has been conducted on selection in Arctic streams. Thus, the objectives of this study were to 1) describe the diet of Ninespine Stickleback across an Arctic tundra watershed and 2) identify dietary preferences, if any. DNA metabarcoding of stickleback gut contents from 14 different Arctic streams was used to examine stickleback diet, and electivity indices were used to determine prey selection. We found that stickleback in Arctic tundra streams fed primarily on chironomids because of their high abundance in the stream community, but stickleback showed higher preference for taxa known to provide higher caloric content, including Baetidae, Simuliidae, and Tipulidae. Diet and predatory preference differed between streams with cobble–boulder vs silt–pebble substrate types, but Chironomidae generally maintained lower importance than Baetidae, Simuliidae, and Tipulidae in both environments. With the progression of climate change loosening the physiological constraints on temperate invertebrates as they move north, further research should be conducted on how greater access to preferable (or higher nutritional quality) invertebrate food resources may affect Arctic fish populations and stream food webs.
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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".