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Record W4414109648 · doi:10.36939/ir.202509101343

Summer Roosting and Foraging Habitat of Endangered Little Brown Bats (Myotis lucifugus) in Central Canada

2024· dissertation· en· W4414109648 on OpenAlexaboutno aff
Arshiya Bagheri Torbehbar

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsForagingHabitatEndangered speciesPopulationCritically endangeredHabitat destructionEcological trapHibernation (computing)

Abstract

fetched live from OpenAlex

Little brown bats (Myotis lucifugus) are listed as endangered in Canada and by the International Union for the Conservation of Nature (IUCN) due to population declines caused by the fungal disease white-nose syndrome (WNS). Under the Species at Risk Act, conservation efforts must focus on identifying and protecting critical habitats essential for the survival and recovery of listed species. While current efforts for little brown bats emphasize hibernation sites, designating maternity roosts as critical habitats are equally important. Despite some existing data, maternity roosts are not yet designated as critical habitats, but it is crucial as maternity roosts support bats’ recovery from WNS in the spring and successful reproduction and juvenile development in the summer. Understanding both roosting and foraging habitat selection, as well as drivers of foraging behaviour, is important for developing recovery plans, as these habitats are often spatially associated (Balzer et al. 2023). The broad objectives of my thesis are to characterize summer maternity roosts, better quantify roost switching and foraging habitat selection, and better quantify environmental variables that influence bats’ nightly activities. To achieve this, I tagged 30 lactating little brown bats in the summers of 2021 and 2022 (n=15 bats/year) in Ontario, Canada, and tracked them to roosts to identify their roosting habitat selection and quantify roost-switching behaviours. I used data from 2021 (n=15 bats) to understand foraging habitat selection and the influence of factors such as minimum nightly temperatures, maximum wind speed, and air quality on nightly activities and home-range sizes. My Chapter 2 results show that bats predominantly preferred buildings and bat houses over trees, with the largest groups found in buildings, while bats in trees were always solitary. Regardless of roost types, bats preferred structures close to water with southern exposure, possibly to reduce commuting costs to food and drinking areas and to maximize heat gain. These findings should help identify geophysical attributes important for the designation of maternity roosts as critical habitats. Bats in buildings switched roosts significantly less than when they roosted in bat houses or trees, but still switched more than expected, suggesting that management strategies should focus on networks of suitable roosts, rather than individual structures, to meet bats’ roosting requirements. My Chapter 3 results show that bats preferred foraging over wetlands, open water, anthropogenic areas, and forest edges, with bats having larger home-range sizes than observed in comparable studies of this species. Taking advantage of variation in air quality due to wildfire near my study area, I found that bat nightly activities and home-range sizes were significantly affected by air quality, with bats being less active and having smaller home ranges on nights with poor air quality. I found no effect of minimum nightly temperatures or maximum wind speeds on foraging behaviour. These findings underscore the importance of protecting both roosting and foraging habitats to support the recovery of endangered little brown bats.

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 score0.022
Threshold uncertainty score0.068

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 studies0.0010.001
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 head0.218
Teacher spread0.203 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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