Spring cattle grazing and mule deer nutrition in a bluebunch wheatgrass community
Bibliographic record
Abstract
Abstract Although domestic cattle and mule deer (Odocoileus hemionus) share bluebunch wheatgrass (Pseudoroegneria spicata) communities throughout western United States and Canada, no study has yet evaluated the effects of cattle grazing on the nutrient intake of mule deer and the nutritional carrying capacity in these semi‐arid rangelands. We expected that spring cattle grazing would decrease plant biomass available to deer but increase the nutritional quality of forage by arresting the phenology of grasses, reducing the proportion of standing dead biomass of grasses and promoting forbs. Using 0.4‐ha exclosures, we created 3 replicates of paired grazed and non‐grazed treatments within 3 pastures ranging from 64 to 509 ha in bluebunch wheatgrass communities on dry‐stony ecological sites in southeastern Washington, USA. After cattle had grazed 1 of each pair of 0.4‐ha plots to 40% utilization from 10 April to 29 May 2009, we sampled the biomass and measured the digestible energy (DE; kJ/g) and digestible protein (g/100 g feed) of plants that spring and fall. Using hand‐raised, tractable mule deer, bite count methods, and behavioral observations, we measured daily DE and digestible protein intake within each plot. When compared to non‐grazed plots, grazed plots had half the total and 3 times less live plant biomass in spring, and 25% less biomass in fall, whereas DE content of bluebunch wheatgrass and mule deer diets did not differ between grazing treatments. However, daily DE intake of deer was 39% less in spring and 13% less in fall in grazed than non‐grazed plots. Nutritional carrying capacity (deer days/ha) did not vary between grazing treatments except for 1 pasture in fall, indicating that spring grazing by cattle in bluebunch wheatgrass communities did not improve the nutritional quality of deer diets enough to offset the overall loss of live forage biomass. © 2013 The Wildlife Society.
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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 | 0.001 | 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".