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Record W2802160657 · doi:10.3390/d10020029

Restoration of Legacy Trees as Roosting Habitat for Myotis Bats in Eastern North American Forests

2018· article· en· W2802160657 on OpenAlexaboutno aff
Michael J. Lacki

Bibliographic record

VenueDiversity · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatMyotis lucifugusEcologyGeographyPopulationBiologyHabitat destructionOverwintering

Abstract

fetched live from OpenAlex

Most eastern North American Myotis roost in forests during summer, with species forming maternity populations, or colonies, in cavities or crevices or beneath the bark of trees. In winter, these bats hibernate in caves and are experiencing overwinter mortalities due to infection from the fungus Pseudogymnoascus destructans, which causes white-nose syndrome (WNS). Population recovery of WNS-affected species is constrained by the ability of survivors to locate habitats suitable for rearing pups in summer. Forests in eastern North America have been severely altered by deforestation, land-use change, fragmentation and inadvertent introduction of exotic insect pests, resulting in shifts in tree distributions and loss of large-diameter canopy-dominant trees. This paper explores patterns in use of tree roosts by species of Myotis across Canada and the United States using meta-data from published sources. Myotis in western Canada, the Northwest, and Southwest selected the largest diameter roost trees and also supported the largest maximum exit counts. Myotis lucifugus, M. septentrionalis and M. sodalis, three species that inhabit eastern forests and which are currently experiencing region-wide mortalities because of WNS, selected roosts with the smallest average diameters. Recovery efforts for bark- and cavity-roosting Myotis in eastern North American forests could benefit from management that provides for large-diameter trees that offer more temporally-stable structures for roosting during the summer maternity season.

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 score0.355
Threshold uncertainty score0.655

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 head0.037
GPT teacher head0.242
Teacher spread0.205 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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