Decadal Heat and Drought Drive Body Size of North American Bison (Bison bison) Along the Great Plains
Bibliographic record
Abstract
Large grazers are visible and valuable indicators of the effects of projected changes in temperature and drought on grasslands. The grasslands of the Great Plains have supported the greatest number of bison (Bison bison; Linnaeus, 1758) since prehis-toric times. We tested the hypothesis that body mass (BM, kg) and asymptotic body mass (ABM, kg) of Bison decline with rising temperature and increasing drought over both temporal and spatial scales along the Great Plains. Temporally, we modeled the relationship of annual measures of BM and height (H, m) of 5,781 Bison at Wind Cave National Park (WICA) from 1966 to 2015. We used Gompertz equations of BM against age to estimate ABM in decadal cohorts; both females and males decreased from the 1960s to the 2010s. Male ABM was variable but consistently larger (699 vs. 441 kg) than female ABM. We used local mean decadal temperature (MDT) and local mean decadal Palmer Drought Severity Index (dPDSI) to model the effects of climate on ABM. Drought decreased ABM temporally (−16 kg/local dPDSI) at WICA. Spatially, we used photogrammetry to measure body height (HE) of 773 Bison to estimate BMEin 19 herds from Saskatchewan to Texas, including WICA. Drought also decreased ABM spatially (−16 kg/local dPDSI) along the Great Plains. Temperature decreased ABM both temporally at WICA (−115 kg/°C local MDT) and spatially (−1 kg/°C local MDT) along the Great Plains. Our data indicate that temperature and drought drive Bison ABM presumably by affecting seasonal mass gain. Bison body size is likely to decline over the next five decades throughout the Great Plains due to projected in-creases in temperatures and both the frequency and intensity of drought.
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 | 0.000 | 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) | 0.001 | 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".