Increasing fish biodiversity in high elevation Albertan lakes in response to global environmental change over the past 50 years
Bibliographic record
Abstract
Introduction The diversity of freshwater fishes is threatened by multiple environmental stressors, including climate change, alterations in land use, and introduction of non-native species. However, the quantification of temporal biodiversity in freshwater communities is limited. Here, we asked: i) how has alpha (species richness), beta (changes in freshwater species composition), and gamma diversity (total species diversity in a landscape) changed over time for lakes over a 50 year period?; and ii) What are the climatic, land use, and lake morphological drivers associated with higher diversity? Methods We assembled a database of fish species occurrence from 20 lakes across subalpine and alpine regions in Alberta from 1970-2019, in addition to lake morphological, climatic, and land use characteristics of the watersheds. Results We observed an overall increase in alpha, beta, and gamma diversity from the 1970s to 2009s. However, all measures of diversity declined from 2010-2019. We found that more lakes and species assemblages were influenced by species gains, rather than species losses (with the exception of the last decade of sampling). Discussion Generally, we found that coolwater species were expanding and coldwater fishes were being lost throughout our study lakes. We highlight temporal heterogeneity in fish biodiversity responses to substantial environmental pressures in this region.
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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 | 0.001 | 0.001 |
| 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".