Sensitivity of multiple vital rates for ruffed grouse in the upper Great Lakes region
Bibliographic record
Abstract
Effective management of wildlife requires a full understanding of population dynamics and knowledge of potential drivers that influence population growth. The Ruffed Grouse (Bonasa umbellus) is a popular upland game bird widely distributed across the northern United States and Canada that has experienced population declines within portions of its range in response to forest maturation and habitat loss. Although the species has been extensively studied, few efforts have been made to synthesize demographic data into a sensitivity analysis to guide management actions. We reviewed the literature and compiled Ruffed Grouse vital rates from 14 field studies conducted across four decades (1982−2018) within the Upper Great Lakes region of Michigan, Minnesota, and Wisconsin, USA. We parameterized a deterministic matrix model to evaluate population dynamics and conducted sensitivity analyses to identify vital rates projected to have the greatest influence on the finite rate of population change (λ). Our modeling effort projected a stable but highly variable annual rate of population change (λ = 1.01; 95% CI = 0.88–1.14) for Ruffed Grouse in the Upper Great Lakes region. Stochastic rates of population change derived from spring drumming surveys (λ = 1.01; 95% CI = 0.61–1.45) and Christmas Bird Count surveys (λ = 0.99; 95% CI = 0.62–1.76) of the corresponding regional population provided validation of stable trends over the same time period as our demographic model. Prospective elasticities and variance-scaled sensitivities suggested λ would be greatly influenced by components of reproductive performance: nesting success, chick survival, and post-fledging juvenile survival. Retrospective analysis indicated that much of the overall variability in λ and annual productivity was also attributed to annual variation in nesting success. Management of this species has often focused on fall and overwinter survival, but population projection models provided little evidence that survival was the predominant factor affecting population growth of Ruffed Grouse in this region. A suite of confounding factors and demographic processes that drive population trends can differ significantly across a species’ range. In the Upper Great Lakes region, management efforts aimed at maximizing reproductive success would likely have the greatest potential influence on Ruffed Grouse population growth. Other types of systematic, regional survey data can also be useful for validating population trends derived from demographic modeling studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".