Predicting mule deer recruitment from climate oscillations for harvest management on the northern Great Plains
Bibliographic record
Abstract
ABSTRACT We analyzed a unique 51‐year time series for a population of mule deer in the North Dakota badlands, USA to examine the effects of seasonal weather on autumn recruitment. Winter weather recorded prior to birth of fawns and weather conditions recorded during spring the previous year (lagged effect), but not during spring or summer after birth, were related to observed patterns in autumn recruitment. When deer density was low (approx. 1 deer/km 2 ) during the 1960s, fawn/female ratios were high ranging from 1.1 to 1.4 when minimum temperatures during the prior winter averaged −16° C and −8° C, respectively. Likewise, during the 2000s, when deer density was high (approx. 3 deer/km 2 ), fawn/female ratios ranged from 0.6 to 0.9 when minimum daily temperatures during the previous winter were −16° C and −8° C, respectively. Large‐scale Pacific‐based climatic indices (>2,000 km to the Pacific coast) were correlated with local weather and helped explain variability in autumn recruitment. Higher values of the multivariate el niño southern oscillation and Pacific decadal oscillation were correlated with warmer and drier winters in the North Dakota badlands, whereas the North Pacific Index was correlated with colder and snowy winters. The ability to predict recruitment from local weather or from broad‐scale climate indices (oscillations) provides greater opportunities for conservation and management, such as adjusting harvest quotas prior to autumn harvest. © 2015 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".