Linking forest management to moose population trends: The role of the nutritional landscape
Bibliographic record
Abstract
Forested lands in the western USA have undergone changes in management and condition that are resulting in a shift towards climax vegetation. These changes can influence the quality and quantity of forage for herbivores that rely on early-seral plants. To evaluate how management of forested landscapes might affect nutrition for Shiras moose (A. a. shirasi) at large spatial scales, we focused on shrubs and evaluated summer diet composition, forage availability, and forage quality across 21 population management units encompassing >36,000 km2 in northern Idaho, USA. We identified 17 shrub species in the diets of moose, 11 of which comprised the bulk of the diets. These forage shrubs varied markedly in both energy (mean digestible energy for leaves ranged from 9.62 to 12.89 kJ/g) and protein (mean digestible protein for leaves ranged from 1.73 to 7.90%). By adapting established field sampling methods and integrating recent advances in remote sensing analyses in a modeling framework, we predicted approximations of current and past (i.e., 1984) quantities of forage shrubs across northern Idaho. We also created a qualitative index of population trend for moose across population management units using harvest data. Predicted quantities of forage shrubs varied widely across the study area with generally higher values at more northern latitudes. The quantity of forage shrubs was estimated to have declined over the past 30 years in about half of the population management units, with the greatest declines predicted for high-energy forage species. The population trend index was correlated with the percent change in availability of moderate-energy forage shrubs, indicating that availability of forage shrubs and change in availability over time might be affecting population dynamics for moose in northern Idaho. Our study highlights the importance of assessing how changes in forest management across broad spatiotemporal extents could affect wildlife and their habitats.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".