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Asymmetrical competition between Neotropical dung beetles and its consequences for assemblage structure

2005· article· en· W2066905769 on OpenAlexaff
Finbarr G. Horgan, René C. Fuentes

Bibliographic record

VenueEcological Entomology · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBiologyInterspecific competitionSpecies richnessEcologyCompetition (biology)Species diversityPopulation

Abstract

fetched live from OpenAlex

Abstract. 1. This study combines the results of laboratory experiments using representative assemblage components and pitfall trapping over a large geographical area to examine the hypothesis that ongoing interspecific competition structures Neotropical dung beetle assemblages. 2. From Guatemala to Panama assemblages of large to medium‐sized, fast‐tunnelling dung beetles include a single large, nocturnal dichotomiine species, Dichotomius annae (Kohlmann & Solís, 1997). In competition experiments, this species out‐competed the medium‐sized coprine species, Copris lugubris Boheman and Phanaeus demon Laporte‐Castelnau, for dung and nesting space, in spite of earlier colonisation by the diurnal species, P. demon . 3. Differences in the abundance of D. annae at Central American sites did not affect total fast‐tunnelling dung beetle assemblage richness over the rainy season. However, D. annae rank order was directly related to the probability of interspecific encounters (Hurlbert's Δ 1 ) among species. These trends were also observed when species lists from published and unpublished studies of other large allopatric dichotomiine species, with a more northerly distribution, were included in the analyses. 4. The results obtained suggest that where large dichotomiine species are abundant, their efficient pre‐emption of a considerable proportion of available resources drives all, or most, other fast‐tunnelling species to a lower population density, thereby decreasing assemblage diversity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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.

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

Citations40
Published2005
Admission routes1
Has abstractyes

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