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Record W4379388848 · doi:10.1242/jeb.245008

Batting around a new idea: attracting insect buffets for endangered bats

2023· article· en· W4379388848 on OpenAlexaboutno aff
Kristina A. Muise

Bibliographic record

VenueJournal of Experimental Biology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMyotis lucifugusEndangered speciesHibernation (computing)CaveBiologyEcologyWhite (mutation)ZoologyCaptive breedingGeographyHabitat

Abstract

fetched live from OpenAlex

During the autumn months, many bat species face challenges to find sufficient food and generate the large fat stores they require to survive winter. However, North American bats face an additional challenge: a fungal disease called white-nose syndrome. The disease causes bats to arouse too frequently during hibernation, eventually starving to death before spring. Twelve bat species are currently affected, and some are listed as Endangered in either the USA or Canada. Winifred Frick, from Bat Conservation International and the University of California, Santa Cruz, USA, with a team of collaborators from various US and Canadian institutions, set out to test a new conservation approach that could protect bats by increasing the number of flying insects in an area, allowing the animals to gorge before and after hibernation.To test their approach, the team chose caves in the Upper Peninsula of Michigan, USA, which were known to have ∼100 remaining hibernating little brown bats (Myotis lucifugus) after declines from white-nose syndrome. They then monitored five caves in the autumn of 2019 (1 September–4 October), when the bats were fattening for hibernation, and four caves in the spring of 2021 (19 April–25 May), when bats that survived white-nose syndrome were emerging from hibernation. To attract flying insects, Frick and colleagues placed a single UV light 250 m from each cave entrance, 3 m above the ground. In the autumn, the team alternated nights when the UV lights were on, to assess changes in the number of insects they attracted. However, in the spring, to determine whether the bats could learn to respond to changes in the numbers of insects available to dine on, some of the caves were provided with a UV light on each night, while others had no UV lights on.The team also measured insect abundance every night during the autumn but only once a week during the spring, using a funnel and bucket to trap insects that were then brought back to the lab to be weighted and identified. To monitor bat foraging activity at the caves, the researchers used bat detectors placed at the sites to record the bats’ ultrasonic calls while flying from sunset to sunrise each night during the autumn and spring. Back in the lab, the recordings were reviewed to identify and count the number of ‘feeding buzzes’ – the echolocation calls that bats make when successfully catching insects – to determine the bats’ foraging success rates.The researchers found that when the UV lights were on during the autumn fattening period, the bats successfully captured and ate three times more insects than on nights when the UV lights were off. Additionally, the team collected a far greater mass of insects (16.7 times more) on the UV illuminated nights. Additionally, in the spring, the hunting bats were 8.5 times more successful in capturing prey, and the mass of insects collected by the team was 26.1 times greater at the locations where the UV lights were on than at the locations where the UV lights were off. These results show that UV lights in the autumn and spring increase the insect abundance for bats to eat, and that bats learn to forage more when there is increased food availability.Overall, Frick and colleagues have successively shown a novel approach for artificially attracting greater numbers of insects for bats to feast on during the critical spring and autumn periods. The results from the study could aid in the recovery of bat populations from white-nose syndrome and inspire wider conservation approaches for critically endangered hibernating bats in North America.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.093
GPT teacher head0.321
Teacher spread0.228 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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