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Record W3030474153 · doi:10.1111/1365-2664.13687

Recovery of a boreal ground‐beetle (Coleoptera: Carabidae) fauna 15 years after variable retention harvest

2020· article· en· W3030474153 on OpenAlexafffundabout
Linhao Wu, Fangliang He, John R. Spence

Bibliographic record

VenueJournal of Applied Ecology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Conservation Association
KeywordsDeciduousBorealBiodiversityUnderstoryEcologyTaigaHabitatDisturbance (geology)FaunaClearcuttingEnvironmental scienceBiologyGeographyAgroforestryCanopy

Abstract

fetched live from OpenAlex

Abstract Retention harvests are preferred over traditional clear‐cuts for sustainable forest management because maintenance and re‐establishment of native forest biodiversity is a priority. However, few studies have examined long‐term responses of biotic assemblages to retention harvest at particular sites. We studied the effects of decreasing initial harvest intensities (clear‐cut, 10%, 20%, 50% and 75% dispersed green‐tree retention) on carabid beetle assemblages relative to assemblage changes in un‐harvested control stands in four successionally ordered cover‐types of boreal mixedwood forest. We also studied temporal effects by comparing assemblages over a 16‐year pre‐ and post‐harvest period, using data collected through monitoring of the EMEND (Ecosystem Management Emulating Natural Disturbance) experiment in NW Alberta, Canada. Retention harvests affected assemblages differently across cover‐types. Assemblages in compartments harvested in the earlier forest successional stages of ‘deciduous’ or ‘deciduous with spruce understorey’ converged towards the pre‐harvest structure of corresponding controls over time. In contrast, beetle assemblages in ‘mixed’ or ‘conifer’ compartments, that represent later successional forest, moved steadily away from their pre‐harvest structures during the first post‐harvest decade. These latter assemblages became strikingly more similar to those under deciduous canopies by 15‐year post‐harvest. Synthesis and applications . Variable retention harvests will promote and maintain biodiversity better than clear‐cutting. Higher retention levels promote faster recovery, but towards fauna typical of early successional forest in all cover‐types. Carabids associated with conifer habitats are less resistant to impact from harvesting than are those from broadleaf deciduous forest. Therefore, conifer‐dominated stands present the most significant management challenge and higher retention levels are required to promote rapid and effective faunal recovery in such late successional stands.

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 score0.115
Threshold uncertainty score0.575

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)0.0010.000

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 head0.014
GPT teacher head0.181
Teacher spread0.167 · 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

Citations32
Published2020
Admission routes3
Has abstractyes

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