Offshore Prey Densities Facilitate Similar Life History and Behavioral Patterns in Two Distinct Aquatic Apex Predators, Northern Pike and Lake Trout
Bibliographic record
Abstract
Abstract Northern PikeEsox luciusare important aquatic apex predators in freshwater ecosystems across the Canadian Boreal Shield. Although Northern Pike have historically been described as nearshore ambush predators, larger individuals have been anecdotally observed foraging in offshore habitats. We used two province‐wide data sets from Ontario, Canada, to investigate the degree to which Northern Pike are generalist predators by examining the influence of offshore prey fish densities on their life histories. To better understand whether the life history patterns observed were unique to Northern Pike or representative of aquatic apex predators generally, we compared Northern Pike life history and catch results to those of the Lake TroutSalvelinus namaycush, a well‐known pelagic apex predator. We found that the asymptotic lengths of both Northern Pike and Lake Trout were positively related to CiscoCoregonus artediCPUE. Furthermore, both Northern Pike and Lake Trout occupied offshore habitat more frequently in lakes with greater CiscoCPUEs. Northern Pike early growth and mortality rates were negatively related to CiscoCPUEbut positively related to Yellow PerchPerca flavescensCPUE, suggesting that Northern Pike undergo ontogenetic shifts to foraging on Ciscoes later in life. Although the growth and mortality of these predators were related to prey availability, variation in theCPUEs of Northern Pike and Lake Trout was best explained by physical lake characteristics. Our study suggests that Northern Pike and Lake Trout respond similarly to CiscoCPUEacross the Canadian Boreal Shield, consistent with research reported for other aquatic apex predators. Results of this work collectively suggest that generalist behavior and large‐bodied life history strategies of Northern Pike are facilitated by the availability of Ciscoes.
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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 | 0.000 | 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) | 0.001 | 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".