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Bat Mobility and Roosts in a Fragmented Landscape in Central Amazonia, Brazil

2003· article· en· W2173367494 on OpenAlexafffund
Enrico Bernard, M. Brock Fenton

Bibliographic record

VenueBiotropica · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorYork University
KeywordsEcologyHabitatForagingGeographyFragmentation (computing)Amazon rainforestHabitat fragmentationForest fragmentationFrugivoreBiology

Abstract

fetched live from OpenAlex

ABSTRACT In spite of the important role played by bats in tropical ecosystems, little is known about how they are affected by habitat fragmentation. By using a mark/recapture protocol and radiotelemetry techniques in a naturally fragmented landscape composed of primary forests and forest fragments surrounded by savannas in Alter do Chao, Para State, Brazil, we were able to track the movements of various species of bats, calculate the size of the area used, locate roosts and potential feeding areas, and determine preferred flight routes. We marked 3440 bats belonging to 44 species and recaptured 151 belonging to 14 species. The average distance between extra‐site recaptures was 2.2 km. With the exception of bats marked in fragments and recaptured in forests, all other possible inter‐habitat recaptures were observed. We selected 23 bats of 8 species for radiotelemetry and the areas used by them varied from 65 to 530 ha. Some species restricted their activity to the vicinity of their roosts, rarely moving more than 500 m away, but others traveled greater distances between roosts and foraging areas. All tracked bats flew over savannas, crossing distances from 0.5 to 2.5 km. Roost location and type varied among species, from individuals roosting alone in the foliage to colonies in buildings. Bats were highly mobile and savannas did not appear to inhibit the movements of some species, suggesting that a persistent biological flow may be maintained among isolated fragments, with bats acting as pollinators and seed dispersers. RESUMO Apesar da importa̧ncia dos morcegos nos ecossistemas tropicais, pouco se sabe a respeito de como estes animais interagem com uma paisagem fragmentada. Apresentamos aqui dados sobre a mobilidade de morcegos em uma paisagem naturalmente fragmentada, composta por florestas e fragmentos florestais circundados por savanas em Alter do Chão, Estado do Pará, Brasil. Através de marcação/recaptura e de rádio‐telemetria conseguimos rastrear os movi‐mentos de espécies selecionadas, calcular o tamanho da área usada, localizar abrigos e áreas potenciais de forrageio e apontar rotas preferidas de vo̧o. Marcamos 3440 morcegos de 44 espécies e recapturamos 151 individuos de 14 espécies. A dista̧ncia média entre recapturas em sítios diferentes foi de 2.2 km. Com exceção dos morcegos que foram marcados em fragmentos e recapturados em florestas, todas as outras combinações de recapturas entre habitats foram observadas. Selecionamos 23 morcegos de oito espécies para radio‐telemetria e as áreas usadas por eles variaram entre 65 e 530 ha. Algumas espécies restringiram suas atividades ao redor dos abrigos, raramente afastando‐se mais do que 500 m destes, enquanto outras deslocaram‐se por dista̧ncias maiores entre os ábrigos e as areas de forrageio. Todos os morcegos rastreados cruzaram as savanas, cobrindo dista̧ncias de 0.5 a 2.5 km. O tipo e a localização dos abrigos variou entre as espécies, desde individuos abrigando‐se sozinhos na folhagem até colo̧nias em edificações. Os morcegos apresentaram alta mobilidade e as savanas aparentemente não inibiram a movimentação de algumas espécies, sugerindo que um fluxo boilógico entre fragmentos pode persistir, tendo os morcegos como agentes polinizadores e dispersores de sementes.

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)0.001
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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.208
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".

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Citations218
Published2003
Admission routes2
Has abstractyes

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