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Record W1977065738 · doi:10.1071/wf08109

Effect of fire severity on long-term occupancy of burned boreal conifer forests by saproxylic insects and wood-foraging birds

2010· article· en· W1977065738 on OpenAlexaffabout
Antoine Nappi, Pierre Drapeau, Michel Saint‐Germain, Virginie A. Angers

Bibliographic record

VenueInternational Journal of Wildland Fire · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsWoodpeckerTaigaSnagBorealEcologyOccupancySalvage loggingForagingBiologyAbundance (ecology)ForestryGeographyHabitat

Abstract

fetched live from OpenAlex

Fire severity can vary greatly within and among burns, even in the Canadian boreal forest where fire regimes consist mostly of stand-replacing fires. We investigated the effects of fire severity on the long-term occupancy of burns by (i) saproxylic insects and (ii) three wood-foraging birds. Based on observations made 6 to 11 years after fire in burned conifer forests that varied in fire severity in Quebec, Canada, our results indicate that low-severity portions of the burns likely provided snag conditions suitable for the long-term presence of deadwood-associated insects and birds. The black-backed woodpecker, a post-fire forest specialist, was still abundant 6 and 8 years after fire. This pattern was likely explained by the persistence of several saproxylic insect species that are associated with recently dead trees and by the positive effect of lower fire severity on the abundance of Arhopalus foveicollis, a cerambycid with a long life cycle in dead wood. The American three-toed woodpecker and the brown creeper, and their associated prey (Scolytinae beetles), were more abundant in burned stands of lower v. higher severity. We conclude that less severely burned snags and stands within high-severity burns may favour the long-term presence of trophic webs that involve saproxylic insects and wood-foraging birds in burned boreal forests.

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 score0.024
Threshold uncertainty score0.171

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)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.237
Teacher spread0.229 · 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

Citations96
Published2010
Admission routes2
Has abstractyes

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