Reproductive phenology and behaviour of endangered redside dace (<scp><i>Clinostomus elongatus</i></scp>) in urban streams
Bibliographic record
Abstract
Investigation of the reproductive phenology and spawning behaviour of imperilled species in relation to environmental variability is needed to understand a critical component of species life history. In this study, we used redside dace (Clinostomus elongatus), a freshwater leuciscid listed as Endangered under Canada's Species at Risk Act, to model spawning phenology and make predictions about spawning initiation using historical and climate change projected thermal cues (measured as cumulative growing degree days), and provide an ethological description of spawning behaviour. Logistic regression models applied to 4 years of average daily stream water temperature data and field behavioural observations of the onset of spawning activity indicated a 50% probability of spawning initiation when cumulative growing degree days reached 214°C days and a 95% probability of spawning initiation at 288°C days. Using two climate change scenarios (i.e., a mid-century 1.6°C increase and an end of century 3.6°C increase), spawning initiation was predicted to advance 3 days by the year 2050 and 7 days by the year 2100. Underwater video cameras placed at two sites within an urban stream captured 73 unique spawning events revealing that redside dace spawn in pairs as well as in dense, tightly packed groups (more than 20 individuals). Moreover, there is evidence of redside dace having a polygynandrous mating system, as female redside dace spawned with multiple males in 45.2% of the total spawning events recorded. Taken together, this study provides important insights into redside dace spawning initiation and behaviour, key life-history traits having conservation implications for future reproductive success and, ultimately, population dynamics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".