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Record W2271683748 · doi:10.1139/cjz-2015-0167

Influence of forest composition and age on habitat use by bats in southwestern British Columbia

2016· article· en· W2271683748 on OpenAlexafffundvenueabout
Tanya M.J. Luszcz, Robert M. R. Barclay

Bibliographic record

VenueCanadian Journal of Zoology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Calgary
FundersCanadian Forest ServiceNatural Sciences and Engineering Research Council of CanadaU.S. Forest Service
KeywordsForagingHabitatEcologyBiologyInsectivoreRiparian forestRiparian zone

Abstract

fetched live from OpenAlex

Habitat use by bats in forests varies with environmental variables, clutter, and prey availability, but few studies have looked at habitat use as a function of forest composition and forest age across a range of elevations. We examined factors influencing habitat use of forest-dwelling bats in southwestern British Columbia using ultrasonic detection. We measured activity of three bat foraging guilds in four forest types and three age classes from May to August 2000 and 2001. Habitat use varied with forest composition and stand age, but not always as we predicted. Activity of open-habitat foragers (large bats) was greatest in higher elevation forests and in young forests. In contrast, activity of the long-eared myotis (Myotis evotis (H. Allen, 1864)), a gleaner, was greater in lower elevation black cottonwood (Populus trichocarpa Torr. & A. Gray ex Hook.) and cedar–hemlock stands. Myotis evotis activity also increased with increasing forest age. Activity of edge and gap insectivores (genus Myotis Kaup, 1829) was greater in black cottonwood stands and old Interior Douglas-fir stands than in other forest types. The probability of detecting foraging Myotis was highest in black cottonwood stands. Myotis activity did not show a clear pattern with forest age. Our results demonstrate the importance of black cottonwood riparian forests to foraging Myotis, as well as the importance of older, low-elevation 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.180
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2016
Admission routes4
Has abstractyes

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