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Record W2002699879 · doi:10.1371/journal.pone.0095703

Different Relationships between Temporal Phylogenetic Turnover and Phylogenetic Similarity and in Two Forests Were Detected by a New Null Model

2014· article· en· W2002699879 on OpenAlexaff
Jianxiong Huang, Jian Zhang, Yong Shen, Honglin Cao, Wanhui Ye, Linfang Wu, Yue Bin

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Alberta
FundersChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsPhylogenetic treeNull modelBiological dispersalBiologySimilarity (geometry)PhylogeneticsEcologyCommunityEvolutionary biologyGeneticsDemographyArtificial intelligencePopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Ecologists have been monitoring community dynamics with the purpose of understanding the rates and causes of community change. However, there is a lack of monitoring of community dynamics from the perspective of phylogeny. METHODS/PRINCIPLE FINDINGS: We attempted to understand temporal phylogenetic turnover in a 50 ha tropical forest (Barro Colorado Island, BCI) and a 20 ha subtropical forest (Dinghushan in southern China, DHS). To obtain temporal phylogenetic turnover under random conditions, two null models were used. The first shuffled names of species that are widely used in community phylogenetic analyses. The second simulated demographic processes with careful consideration on the variation in dispersal ability among species and the variations in mortality both among species and among size classes. With the two models, we tested the relationships between temporal phylogenetic turnover and phylogenetic similarity at different spatial scales in the two forests. Results were more consistent with previous findings using the second null model suggesting that the second null model is more appropriate for our purposes. With the second null model, a significantly positive relationship was detected between phylogenetic turnover and phylogenetic similarity in BCI at a 10 m×10 m scale, potentially indicating phylogenetic density dependence. This relationship in DHS was significantly negative at three of five spatial scales. This could indicate abiotic filtering processes for community assembly. Using variation partitioning, we found phylogenetic similarity contributed to variation in temporal phylogenetic turnover in the DHS plot but not in BCI plot. CONCLUSIONS/SIGNIFICANCE: The mechanisms for community assembly in BCI and DHS vary from phylogenetic perspective. Only the second null model detected this difference indicating the importance of choosing a proper null model.

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)0.014
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.231
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations7
Published2014
Admission routes1
Has abstractyes

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