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Record W4386553741 · doi:10.24124/2023/59414

Winter roosting ecology of silver-haired bats (Lasionycteris noctivagans) in southern British Columbia

2023· dissertation· en· W4386553741 on OpenAlexafffundabout
Emily de Freitas

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of GuelphUniversity of Northern British Columbia
FundersUniversity of Northern British ColumbiaMitacsWildlife Conservation Society
KeywordsMicroclimateTorporEcologyCaveHibernation (computing)GeographyAbundance (ecology)Range (aeronautics)BiologyThermoregulation

Abstract

fetched live from OpenAlex

Many animals spend a considerable amount of time within a shelter, and shelter availability can influence the distribution, abundance, and diversity of animal populations. Shelters used during winter, when ambient temperatures are low (< 0°C), must provide animals with adequate protection to survive through months of adverse conditions. For bats, shelters (roosts) used during winter must also provide microclimates that support energetic requirements for hibernation. Silver-haired bats (Lasionycteris noctivagans) are a tree-roosting species that are migratory throughout much of their range. In parts of northwestern North America, however, they may hibernate locally. Hibernation sites (hibernacula) in regions where winter temperatures are below freezing are typically caves, underground mines, or rock crevice features, as these protect bats from cold temperatures and provide a humid environment that prevents dehydration. In a forested area near Beasley in southern British Columbia, silver-haired bats use an abandoned mine, rock crevices, and trees as roosts during winter. The use of trees as roosts during winter in cold regions (average temperatures < 0°C) is poorly understood. I sought to investigate the winter ecology of tree-roosting silver-haired bats in southern British Columbia. In Chapter 2, I described the characteristics of trees used as hibernacula by silver-haired bats. I hypothesize that bats select winter roost trees non-randomly, and differently across seasons. In Chapter 3, I investigated microclimates and torpor patterns inside silver-haired bat mine, tree, and rock crevice hibernacula. I hypothesize that microclimates vary among winter roost types, and because of these differences, torpor, arousal and movement patterns will differ among tree, rock crevice and mine winter roosts. A majority (66.7%, n = 22) of silver-haired bats used trees as winter roosts during the study. Winter tree roosts were selected non-randomly, and compared to summer roosts, bats used features that provided insulation, such as, cavities in large-diameter, low-decay trees in areas of low canopy closure. Winter trees roosts were colder but more humid compared to the mine and rock crevice roosts. Bats switched among roosts throughout winter depending on ambient conditions but did not alter torpor patterns among roost types. Bats typically used humid roosts (trees) on drier days, and less humid roosts (mine) on more humid days. Further, bats used the well-insulated mine on colder days, and poorly insulated trees and rock crevice roosts on warmer days. Despite this, bats showed more tolerance for colder roost temperatures than expected, remaining in trees during periods of cold (-9°C) temperatures. I conclude that there may be a trade-off between roost humidity and temperature, with bats using multiple hibernacula to optimize energetic benefits in the context of ambient conditions. Trees are an important part of silver-haired bat winter ecology. The use of trees as hibernacula needs to be better understood to inform forest management decisions that better support silver-haired bat conservation by protecting important winter roosts

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)0.000
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 score0.120
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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