Historical, abiotic, and biotic drivers influence contemporary lacustrine fish community composition in the glacial Lake Agassiz basin
Bibliographic record
Abstract
Historical determinants and local ecological filters tend to drive community assembly at large and smaller spatiotemporal scales, respectively. We examined the effects of differential contact to the Wisconsinan deglaciation dispersal corridor, Lake Agassiz, and habitat heterogeneity and species interactions on contemporary lacustrine fish community composition in 261 northwestern Ontario lakes. Species composition varied significantly across lakes grouped by their most recent glacial lake coverage, driven by turnover, and the number of connections (not-covered, single, and secondary contact) to the dispersal corridor over time, explained by nestedness. Maximum lake depth, elevation, and most recent stage of glacial lake coverage were dominant drivers of community composition patterns; and most of the significant species co-occurrence relationships were positive. While the dominant environmental variables reflect the effects of habitat heterogeneity and isolation from the dispersal corridor, the species co-occurrence analysis results reflect the influence of environmental niche filtering and biotic interactions in shaping lacustrine fish communities. The spatiotemporal evolution of Lake Agassiz played a role as a historical determinant, and habitat heterogeneity and local species interactions were ecological determinants.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".