Patterns and drivers of native, non-native, and at-risk freshwater fish richness in Canada
Bibliographic record
Abstract
Understanding the relative roles of the historical, environmental, and anthropogenic processes underlying spatial biodiversity patterns is crucial to predict the impacts of global environmental changes. We quantified the relative roles of these factors in influencing species richness of total, native, non-native, and at-risk freshwater fishes in 985 tertiary watersheds across Canada while accounting for correlations among descriptors and spatial autocorrelation. Our findings indicate differences in factors influencing richness patterns among species categories. Environmental factors related to energy availability and historical factors related to post-glacial recolonization both played roles in shaping spatial variation in native species richness. In contrast, variation in non-native species richness was largely related to human activities increasing propagule pressure and habitat disturbance, which were greater for foreign species (i.e., not native to Canada) than for translocated native species. Anthropogenic processes and environmental conditions were both important determinants of at-risk species richness. Our study emphasizes the importance of an integrated approach that simultaneously considers natural and anthropogenic processes to better predict the fish biodiversity change at the landscape scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".