Environmental drivers of fish functional diversity and composition in the Lower Colorado River Basin
Bibliographic record
Abstract
Freshwater conservation efforts require an understanding of how natural and anthropogenic factors shape the present-day biogeography of native and non-native species. This knowledge need is especially acute for imperiled native fishes in the highly modified Lower Colorado River Basin (LCRB), USA. In the present study we employed both a taxonomic and functional approach to explore how natural and human-related environmental drivers shape landscape-scale patterns of fish community composition in the LCRB. Our results showed that hydrologic alteration, watershed land use, and regional climate explained 30.3% and 44.7% of the total variation in fish community taxonomic and functional composition, respectively. Watersheds with greater dam densities and upstream storage capacity supported higher non-native functional diversity, suggesting that dams have provided additional “niche opportunities” for non-native equilibrium life-history strategists by introducing new reservoir habitat and modifying downstream flow and thermal regimes. By contrast, watersheds characterized by greater upstream land protection, lower dam densities, and higher variation in spring and summer precipitation supported fish communities with a strong complement of native species (opportunistic–periodic strategists). In conclusion, our study highlights the utility of a life-history approach to better understand the patterns and processes by which fish communities vary along environmental gradients.
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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 | 0.001 |
| 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".