Integrating ecological and community science data to understand patterns of colour polymorphism and social behaviour at the northern range limit of a plethodontid salamander
Bibliographic record
Abstract
Traditionally, scientists have relied on ecological surveys to gain information about wildlife; however, community-science data has recently emerged as a valuable resource in organismal research. In this study, we conducted ecological surveys in 23 forests in New Brunswick, Canada and extracted data from iNaturalist across the entire province to understand patterns of Eastern Red-backed Salamanders' colouration and sociality at the northern limit of its range. Ecological data revealed that adult salamanders were more likely to aggregate during the early spring and autumn, reflecting trends observed in other areas of their range. We also compared aggregation behaviour and colouration data between data collection methodologies and found that community-scientists are less likely to report aggregated salamanders and are more likely to report unique colour morphs than ecological surveys. Notably, iNaturalist observations included an amelanistic morph which had yet to be formally documented in New Brunswick. Lastly, we used our ecological survey data to explore if preferences for micro-environmental factors differ between colour polymorphism and aggregated vs. solitary salamanders. There was no evidence for environmental preferences in New Brunswick, which differs from tendencies observed in other populations of this species. Our findings highlight the trade-offs between ecological and community-science approaches and contribute valuable insights into the natural history of P. cinereus at the northern edge of its Canadian range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".