Estimating connectivity between spawning and nursery habitats of northern pike
Bibliographic record
Abstract
Managing fish habitats by focusing on only one apparent habitat type, such as spawning sites, may mislead us by suggesting that, once protected, sites exhibiting high potential for egg deposition will eventually favour recruitment. Some supposedly high potential spawning sites may in fact exhibit low potential for population recruitment when considering their isolation to surrounding nurseries habitats. To maximize survival, post-hatched fish larvae need to access nurseries rapidly. Therefore, it is crucial to estimate precisely the connectivity between such key habitats. We quantified the probability of reaching nursery habitats for larvae of Northern pike (Esox lucius), a species using heterogeneous shallow habitats exposed to large water-level variations. The spatially explicit model developed for >130 km of the St. Lawrence R. allowed us to map the potential spawning and nursery habitats at high resolution. Using two software dedicated for the analysis of connectivity (Anaqualand, Chloe) we quantified spatial metrics, such as the number of sites, the surface of habitats, the proximity index, and the hydrographic distance. Preliminary results revealed a higher fragmentation within narrow sections of the river changing our interpretation of overall value for several presumed high potential spawning sites when considering their connectivity to neighboring nurseries. Habitat fragmentation leading to spatial separation of critical habitat during ontogeny could serve as a significant sink to recruitment.
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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 | 0.002 |
| 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 | 0.001 | 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".