Multiscale impacts of forest degradation through browsing by hyperabundant moose (<i>Alces alces</i>) on songbird assemblages
Bibliographic record
Abstract
Abstract Aim Songbirds are sensitive to changes in forest structure and composition at various spatial scales, particularly during the breeding season. Habitat degradation associated with herbivore browsing may contribute to declines in songbird populations. Here, we evaluate songbird responses to herbivore‐induced habitat change at multiple spatial scales. Location In Gros Morne National Park (GMNP), Newfoundland, Canada, browsing by hyperabundant moose (Alces alces) has changed forest structure by reducing understorey cover and converting regenerating stands to open areas dominated by grasses and shrubs. Methods We conducted point count surveys to measure bird occurrence throughoutGMNPduring the 2010 breeding season. Using vegetation information from ground plots and remote sensing, we characterized habitat at three scales: local, neighbourhood and landscape. Following a two‐step procedure to model species occurrence with habitat, the most important habitat factors within each scale were retained for cross‐scale modelling. Results Cross‐scale models revealed patterns in the association of songbird habitat assemblages with moose‐altered habitats. Early successional species such as mourning warbler (Geothlypis philadelphia) were positively associated with moose‐browsed habitat at the landscape scale. Forest interior specialist (e.g. black‐throated green warbler,Setophaga virens)and generalist species (e.g. boreal chickadee,Poecile hudsonicus) were negatively associated with moose‐browsed habitat at the neighbourhood scale. Local songbird species richness was independent of moose‐browsed habitat at any scale. Main conclusions The influence of intense browsing on forest songbirds varies by species but has the potential to extend beyond the area of immediate impact. Continued intense browsing and resulting forest alteration could cause declines in forest specialists and generalists, but may increase populations of early successional species. To maintain bird assemblages characteristic of the region, we recommend management actions that lower moose density in areas with hyperabundant populations such asGMNPto maintain forest structure and regeneration comparable to that present prior to the introduction of moose.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".