Urbanization correlates with altered growth and reduced survival of a small‐bodied, imperilled freshwater fish
Bibliographic record
Abstract
Abstract Life‐history and other vital rate information is important for effective species conservation by informing demographic trends and aspects of population viability. However, species‐specific information is lacking for many small‐bodied freshwater fishes, which can make it difficult to relate demographic trends to threats or recovery actions. Silver Shiner (Notropis photogenis), a small‐bodied species listed as Threatened under Canada'sSpecies at Risk Act, lacks well‐informed age and vital rate information. We aged 254 Silver Shiner using opercular and otolith structures and determined that Silver Shiner is short‐lived, with a probable maximum age of 4. Three years of field collections (2017–2019) were used to parameterize growth models and to estimate adult (age 1–3+) mortality rates. Silver Shiner captured in urban reaches exhibited altered growth and higher mortality compared to non‐urban reaches, indicating that urbanization may have demographic impacts on the species. Results highlight the importance of detailed age and vital rate information for assessing threats and informing effective conservation approaches for imperilled fishes. Furthermore, the potential negative impact of urbanization on the growth and survival of this species highlights the urgency of better understanding the influence of urbanization on small‐bodied freshwater fishes in general.
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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 | 0.000 |
| 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) | 0.002 | 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".