Reproductive phenology and seasonal mass dynamics of black-tailed prairie dogs (<i>Cynomys ludovicianus</i>) at their northern range limit
Bibliographic record
Abstract
Intraspecific variation is common and can be substantial in species occupying large geographic ranges. For example, populations at a poleward range limit can be exposed to more severe and variable weather, resulting in more punctuated growing seasons and, consequently, large fluctuations in body mass and additional constraints on reproductive phenology. We monitored variation in these traits in a hibernating population of black-tailed prairie dogs (Cynomys ludovicianus (Ord, 1815)) at their northern range limit across four growing seasons. Overall, individual body mass was highly dynamic both within and across growing seasons, and was correlated with sex, the presence of drought, and reproductive effort. This population experienced between-year variation in the timing of reproduction that was associated with weather variation. The influence of weather was particularly evident in 1 year during which a summer–autumn drought was followed by a severe and prolonged winter. This combination led to high overwinter mortality, substantially delayed emergences from hibernation, lower body masses at emergence from hibernation, and complete reproductive failure the following spring. Our results help to emphasize the influence of environmental conditions on levels of phenotypic variation at a species’ northern range limit, which may ultimately contribute to population viability and success.
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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 | 0.001 | 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".