Shifting agriculture and a depleting aquifer: implications of row-crop farming on mule deer population performance
Bibliographic record
Abstract
Context Conversion of native vegetation to cropland is one of the most widespread anthropogenic landscape alterations, particularly in the Great Plains region of the United States. Mule deer occur throughout the Great Plains; however, it is the south-eastern edge of their geographical distribution, and few populations coincide with dense cropland. The rapidly depleting Ogallala Aquifer supplies irrigation to row-crops throughout the region, which will likely shift towards dryland agricultural practices in the near future. Aims We sought to understand how cropland use influences morphology, body condition indices, reproductive output, and survival of free-ranging mule deer. Methods We accumulated a multi-year, longitudinal dataset of movement and morphology for 146 mule deer in the Texas Panhandle. We linked seasonal cropland use with observed morphology, body condition metrics, and reproductive output via linear mixed-effect modelling and assessed the influence of cropland on annual survival by using Cox proportional hazard models. Key results Mule deer that did not use cropland at any time during the year exhibited morphological and nutritional indices similar to those that did; except body-fat percentage being greater for mature (=4-year-old) males that used cropland. Further, cropland use did not predict survival probability. Analyses of cropland use during seasons defined by life-stage showed context-dependent nutritional benefits. Use of cropland during winter following reproduction demonstrated an increase in young (=3-year-old) male antler size and body mass and summer crop use increased body condition for all males. Female mule deer that utilised cropland before pregnancy had increased probability of successful reproduction, demonstrating a potential capital investment strategy in reproduction. Conclusions Cropland does not limit morphology or survival of mule deer; however, additive use of row-crops can provide a nutritional buffer and enhanced reproductive output for individuals that choose to utilise it. Implications Our study demonstrates important population-level interactions with the environment for a species near the extent of their geographical distribution. Conversion of row-crop farming from aquifer depletion or climate shifts may not diminish mule deer populations, but these changes may alter specific habitat-nutritional health relationships that can influence population performance and future conservation efforts.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".