What are the impacts of manipulating grazing and browsing by ungulates on plants and invertebrates in temperate and boreal forests? A systematic review protocol
Bibliographic record
Abstract
Abstract Background Livestock grazing and ‘overabundance’ of large wild herbivores in forested areas have long been perceived as conflicting with the aims of both silviculture and forest conservation; however, certain kinds of herbivory can help to maintain habitat values in forest ecosystems. Management of grazing/browsing in protected forests can, therefore, be a critical tool for biodiversity conservation. However, it is not clear what impacts of wild ungulates or livestock are tolerable or desirable in forests set aside for conservation or restoration. The primary aim of the proposed systematic review is to clarify how the diversity of plants and invertebrates is affected by manipulation of the grazing/browsing pressure by livestock or wild ungulates. The ultimate purpose of the review is to investigate whether such manipulation is useful as a means of conserving or restoring biodiversity in forest set-asides. Methods The review will examine primary field studies of how fencing or other kinds of manipulation of the grazing/browsing pressure by livestock or wild ungulates affects plants or invertebrates. We will consider studies made in boreal or temperate forests anywhere in the world, incorporating investigations made not only in protected areas but also in stands under commercial management. Non-intervention or alternative levels of grazing pressure will be used as comparators. Relevant outcomes include abundance, diversity and composition of plants and invertebrates, tree regeneration, and performance of focal/target species. Relevant studies will mainly be selected from a recent systematic map of the evidence on biodiversity impacts of active management in forest set-asides. A search update will be made with a subset of the search terms used for the systematic map. Searches for additional literature will be made in bibliographies of existing reviews. Relevant studies will be subject to critical appraisal and categorised as having high, medium or low susceptibility to bias. Studies with high susceptibility to bias will be excluded from the review. Useful outcomes and data on interventions and other potential effect modifiers will be extracted from included articles. A narrative synthesis will describe the quality and findings of all studies in the review. Where studies report similar outcomes, meta-analysis will be performed.
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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 | 0.061 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".