Matching Management to Biological Scale: Connectivity among Lacustrine Brook Trout Populations
Bibliographic record
Abstract
Abstract Determining appropriate spatial scales for managing fisheries is a key element of sustainable management. For inland fisheries, management or harvest regulations are often implemented as general regional guidelines or on a lake-by-lake basis. Wild lacustrine brook trout Salvelinus fontinalis from four waterbodies in the Galipo River watershed of Algonquin Provincial Park, Ontario, were used as a model system to determine the extent of population structure among lakes and to identify appropriate scale(s) for managing inland brook trout fisheries. Individual multilocus genotypes were measured using 10 microsatellite loci and were used to characterize movement and gene flow among lakes. Both population- and individual-based analyses showed very little genetic structure among all pairs of waterbodies, suggesting high levels of movement and gene flow between all pairs of lakes. Varying levels of limited genetic structure between pairs of lakes indicate that gene flow may not be uniform within the study system. These results suggest that contrary to previous studies, local watersheds may be the most appropriate management scale for lacustrine brook trout populations in watersheds where physical characteristics provide the potential for migration and gene flow.
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 | 0.001 | 0.002 |
| 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".