MétaCan
Menu
Back to cohort
Record W2588980992 · doi:10.1139/cjb-2016-0184

Vascular epiphyte diversity in two forest types of the “El Cielo” Biosphere Reserve, Mexico

2017· article· en· W2588980992 on OpenAlexvenueno aff
Edilia de la Rosa‐Manzano, Antonio Guerra-Pérez, Glenda Mendieta‐Leiva, Arturo Mora‐Olivo, José Guadalupe Martínez-Ávalos, Leonardo Uriel Arellano-Méndez

Bibliographic record

VenueBotany · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFern and Epiphyte Biology
Canadian institutionsnot available
FundersUniversidad Autónoma de Tamaulipas
KeywordsEpiphyteSpecies richnessEcologyBiologyCloud forestForest ecologySpecies diversityBiodiversityRainforestEcosystemMontane ecology

Abstract

fetched live from OpenAlex

We assessed the diversity, vertical distribution, and the effect of structural and geographical factors on the diversity of vascular epiphyte assemblages (VEAs) in two forest types in the “El Cielo” Biosphere Reserve. We found 2839 epiphyte individuals from 15 species on 240 host trees (27 species) in the semideciduous forest and 5085 individuals from 8 species on 215 host trees (12 species) in the pine–oak forest. Epiphytes showed a preference for the middle zone of the host trees. The diversity of VEAs was uneven in both forest types, although the VEA in the semideciduous forest was the most diverse. VEAs across forests were slightly similar in their composition (30% mean overlap), but very different in their structure (i.e., they did not share very abundant species). The variation in species richness among VEAs was influenced by forest structure, independently of forest type. Compositional differences of VEAs among forest types were explained by geographic distance and host-tree identity (31% and 28% variation explained, respectively). By characterizing the diversity of VEAs we provide a basic understanding of the factors influencing epiphyte diversity in these understudied ecosystems.

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 score0.052
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.034
GPT teacher head0.255
Teacher spread0.221 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueBotanySame topicFern and Epiphyte BiologyFrench-language works237,207