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Record W3095680925 · doi:10.3390/f11111138

Moose Browsing Tends Spruce Plantations More Efficiently Than a Single Mechanical Release

2020· article· en· W3095680925 on OpenAlexafffund
Laurent De Vriendt, Nelson Thiffault, Alejandro A. Royo, Martin Barrette, Jean‐Pierre Tremblay

Bibliographic record

VenueForests · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMinistère des Ressources naturelles et des ForêtsNatural Resources CanadaUniversité LavalCenter for Northern Studies
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsSilvicultureCompetition (biology)Vegetation (pathology)SowingBasal areaHerbivoreBiologyForest managementEcologyCompetitor analysisUnderstoryWildlifeRange (aeronautics)Environmental scienceAgronomyCanopyMedicine

Abstract

fetched live from OpenAlex

Forest vegetation management can improve planted seedling survival and growth and is thus widely used in plantation silviculture. In some jurisdictions, mechanical release using brushsaws has replaced the traditional use of chemical herbicides for forest vegetation management purposes. However, its associated costs and the increasing difficulty of finding qualified labor represent a challenge. The browsing of competition by large herbivores may represent an alternative to mechanical release when planted seedlings are resistant to browsing. Here, we compare the efficacy of moose browsing relative to mechanical release in controlling competing vegetation and in promoting white spruce growth in plantations. In a high moose density region, we used an experimental design consisting of four pairs of moose exclosures and unfenced plots; fifty percent of both the access-restricted and unrestricted study areas received a mechanical release treatment. Moose browsing was more efficient than mechanical release in diminishing the sapling density and basal area of competing species. Mechanical release only reduced the sapling density of taller competitors (height > 201 cm), whereas browsing reduced the sapling densities of competitors across a greater size range (height > 130 cm). These effects of moose browsing on competition translated into a greater positive effect of moose browsing on the basal area of planted spruces. We attribute the higher effectiveness of moose browsing relative to mechanical release to its chronic nature. Moose browsed continuously throughout the year and for multiple years, whereas mechanical release was applied only one time between the second and fourth years after planting. Our results suggest that pairing wildlife management and silviculture decisions could be in the best interest of both the hunting and forestry industries in regions where plantations are frequent and use browse-resistant crop trees. Favouring browsers in controlling the density of competing species could increase the hunting experience and income, while providing an effective, cost-free, and socially acceptable forest vegetation management service.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.019
GPT teacher head0.229
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes2
Has abstractyes

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