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Use of native woodlands and traditional olive groves by foraging bats on a Mediterranean island: consequences for conservation

2007· article· en· W2150678135 on OpenAlexaff
Christina M. Davy, Danilo Russo, M. Brock Fenton

Bibliographic record

VenueJournal of Zoology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsWoodlandHabitatForagingEcologyBiologyMediterranean climateInsectivoreBiodiversityAgroforestry

Abstract

fetched live from OpenAlex

Abstract We recorded bat activity on Zakynthos island (Greece) to test the hypotheses that (1) olive ( Olea europea ) groves and native woodlands provide comparable foraging habitat for insectivorous bats, (2) lower foraging activity occurs in olive groves treated with insecticide chemicals. We acoustically sampled bat activity (passes per minute) in four wooded habitats (organic and non‐organic olive groves, oak woodland ( Quercus ilex and Quercus coccifera ) and pine ( Pinus halepensis ) woodland from June to August 2005. Habitat type did not affect overall bat activity. A single application of insecticide chemicals annually did not affect activity over traditional olive groves. Habitat use on the island differed in several ways from that reported in studies at mainland sites. Most strikingly, pine woodland supported higher bat activity than expected relative to other habitat types, and we recorded unexpectedly high levels of M. capaccinii activity in woodland habitats. We suggest that traditional olive groves buffer some bat species from the effects of deforestation. Conservation plans for Mediterranean bats should consider the biodiversity value of these groves along with the need to conserve small woodland patches. Finally, understanding island‐specific patterns of habitat use is essential to bat conservation on small off‐shore islands.

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)0.001
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 score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.084
GPT teacher head0.263
Teacher spread0.179 · 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

Citations86
Published2007
Admission routes1
Has abstractyes

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