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Record W2518632367 · doi:10.3955/046.090.0304

Short-Term Effects of Variable-Retention Logging Practices on Terrestrial Gastropods in Coastal Forests of British Columbia

2016· article· en· W2518632367 on OpenAlexaffabout
Kristiina Ovaska, Lennart Sopuck, David Robichaud

Bibliographic record

VenueNorthwest Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsASL Environmental Sciences (Canada)
Fundersnot available
KeywordsClearcuttingLoggingGeneralist and specialist speciesAbundance (ecology)HabitatBiodiversityEcologySilvicultureRelative species abundanceBiology

Abstract

fetched live from OpenAlex

Variable-retention (VR) logging practices provide an alternative to clearcutting, but much uncertainty exists on their effectiveness in maintaining biodiversity. We compared patterns of abundance of terrestrial gastropods in areas subjected to either clearcutting or VR treatments, in relation to an uncut control at six experimental sites in coastal British Columbia before and 2–4 years after logging. Gastropods sensitive to the logging treatments in most comparisons included Haplotrema vancouverense, Pristiloma stearnsii and P. lansingi (as a group), and Striatura pugetensis. Several generalist species showed no response to the treatments, and the abundance of two species (Punctum randolphii and Vespericola columbianus) increased in some logged treatments relative to the control. At sites where trees were retained in small groups (0.2–0.5 ha), the abundance of four species was depressed when compared to the control and pre-logging values. No differences among retention levels of 10%, 20%, and 30% were found. At a site where trees were retained in groups of different sizes, large groups (0.8–1.2 ha) were more effective in supporting sensitive species than were small groups (< 0.2 and 0.2–0.5 ha) and clearcuts. At a site where dispersed trees were retained, none of the logged treatments were equivalent to the control. No consistent patterns of higher abundance in the 30% retention level than in the 5% and 10% levels were found. The results contribute to the growing body of information on the complexity of responses of forest floor organisms to habitat modification by logging.

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)0.001
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 score0.284
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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