The species–area relationship for a highly fragmented temperate river system
Bibliographic record
Abstract
Abstract Despite the importance of species–area relationships (SARs) to conservation, SARs in human‐fragmented rivers have received little attention. Our aim was to test for the presence and strength of SARs for littoral fish assemblages of an extensively dammed river in south‐central Ontario, Canada, and to examine long‐running hypotheses for the drivers of SARs. Twenty‐six navigational dams with locks built between 1837 and 1913 occur along the 160 km length of the Trent River examined in this study. We evaluated the relationship between richness and fragment area, and then used linear models to test whether the area per se, habitat diversity, or other hypotheses were best supported by the data. A power–function relationship with area explained 46% of the variation in fish species richness, and the slope (z = 0.4) was high compared with SARs reported from other ecosystems, indicating that species accumulated rapidly with an increase in fragment area. Multi‐predictor models suggested that area was significantly related to richness, but that vegetation cover diversity had a stronger relative effect. The slope of our SAR may indicate that there is a high degree of isolation between populations in different fragments, even though the lock system reportedly allows some passage of organisms. Our findings also suggest that mitigating against local extinction due to small population sizes (i.e., area effects), and enhancing aquatic vegetation cover may be viable strategies for promoting species diversity in the study river. Studies of SARs in fragmented rivers may offer additional benefits to supporting restoration planning where efforts are being made to increase species diversity.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".