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Record W3108266921 · doi:10.1111/aec.12980

Effectiveness of bat boxes for bat conservation and insect suppression in a Western Australian urban riverine reserve

2020· article· en· W3108266921 on OpenAlexaff
Joanna M. Burgar, Yvette Hitchen, J. Blair Prince

Bibliographic record

VenueAustral Ecology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEcologyBiologyFaunaHabitatInsectivorePredationWildlifeLepidoptera genitaliaBiodiversityGeography

Abstract

fetched live from OpenAlex

Abstract Urban wetlands are important habitat for wildlife, particularly insectivorous bats which provide a key ecosystem service in suppressing insects. While claims are often made that bats consume high numbers of mosquitoes in a given night, the evidence for this claim is scant at best. The Canning River Regional Park (CRRP), an urban riverine reserve in Western Australia, has fauna conservation as a primary goal and mosquito control as a top priority. We took advantage of occupied bat boxes within the CRRP to determine the roosting bat species and their diet using non‐invasive DNA metabarcoding of bat faecal samples. The widespread and urban‐adapted Chalinolobus gouldii was the only bat species detected roosting in the bat boxes. This opportunistic forager consumed over 700 unique prey (operational taxonomic units; OTUs); only 14% of OTUs were assigned to either species or genus, representing seven insect orders. Mosquitoes were detected in 11% of the 90 faecal samples, over multiple years and in both the maternity and non‐maternity seasons. Assigned prey was predominantly Lepidoptera with 40% of the 49 Lepidoptera species negatively impacting humans. Urban riverine reserves are critical habitat for bats, which in turn, are crucial in providing the ecosystem service of insect suppression.

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 score0.109
Threshold uncertainty score0.408

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 head0.055
GPT teacher head0.264
Teacher spread0.209 · 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
Published2020
Admission routes1
Has abstractyes

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