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Record W1981649571 · doi:10.1525/cond.2011.100045

Landbird Richness and Abundance in Three Coastal Habitats Near Resorts in Cayo Coco, Cuba

2011· article· en· W1981649571 on OpenAlexafffund
Erin Wiancko, Erica Nol, Alain Parada, Dawn M. Burke

Bibliographic record

VenueOrnithological Applications · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsMinistry of Natural Resources and ForestryTrent University
FundersTrent University
KeywordsSpecies richnessAbundance (ecology)HabitatGuildGeographyEcologyDeciduousMangroveVegetation (pathology)Biology

Abstract

fetched live from OpenAlex

We studied habitat use of disturbed coastal forest by communities of North American migrant and Cuban resident land birds on Cayo Coco, Cuba. This region is subject to a growing resort industry, yet the ecological effects of this disturbance remain largely unknown. Using mist-nets, we sampled birds during two early winters and one late winter. We sampled at sites adjacent to coastal resorts, and at a distance of up to 5.7 km from resorts, in three habitat types (mixed mangrove, semi-deciduous, and coastal scrub forests). We tested for differences in abundance and richness at sites near and far from resorts and among the three habitat types. We also assessed whether bird distribution was associated with fruit abundance and/or vegetation characteristics. Across seasons, migrants were consistently more abundant at sites near resorts than at sites more distant, whereas richness of Cuban residents was consistently higher at sites near resorts than at those farther away. Neither abundance nor richness of the resident guild varied significantly by habitat. The distributions of neither migrants nor residents were correlated with fruit abundance. High foliage density associated with greater edge habitat provided the strongest explanation for high estimates of abundance and richness of migrants and residents near resorts. Our results suggest that moderately disturbed forests near resorts can support abundant and rich communities of both migrants and residents.

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 score0.007
Threshold uncertainty score0.965

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.246
Teacher spread0.218 · 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

Citations5
Published2011
Admission routes2
Has abstractyes

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