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Record W2960003108 · doi:10.1139/cjz-2019-0008

Macroecological approach for scorpions (Arachnida, Scorpiones): β-diversity in Brazilian montane forests

2019· article· en· W2960003108 on OpenAlexvenueno aff
Stênio Ítalo Araújo Foerster, Adriano Medeiros DeSouza, André Felipe de Araújo Lira

Bibliographic record

VenueCanadian Journal of Zoology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVenomous Animal Envenomation and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEcologyRainforestFaunaBiologyMontane ecologyHabitatDeserts and xeric shrublandsSpecies richnessSubalpine forestBiodiversityGeography

Abstract

fetched live from OpenAlex

The montane forests of northeastern Brazil are patches of rainforests, surrounded by xeric vegetation, that originated during the expansion of rainforests in the Pleistocene epoch. Their historical processes make these areas ideal for biogeographical investigations of organisms, particularly in groups with low dispersion and habitat specificity, such as scorpions. We perform a macroecological investigation of the community assembly process of scorpions, disentangling the pattern of β-diversity to test the hypothesis that the similarity in the composition of scorpion fauna in areas of montane forests and coastal rainforests is greater when these localities are geographically close. We also investigated if larger patches of montane forests exhibit a positive species–area relationship. Our results state that species replacement accounts for 71% of the total scorpion β-diversity in montane forest remnants. Additionally, scorpion assemblages were influenced by the spatial arrangement, with a higher similarity between the fauna of montane forests and coastal forests when these areas were geographically close. We did not find a species–area relationship in montane forest patches. The expressive contribution of species replacement to the overall β-diversity may reflect both the high environmental heterogeneity and the historical and independent colonization events that took place in these areas.

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 score0.072
Threshold uncertainty score0.944

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 head0.017
GPT teacher head0.232
Teacher spread0.215 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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