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Record W2906014818 · doi:10.1111/jvs.12708

Variation in compositional and structural components of community assemblage and its determinants

2018· article· en· W2906014818 on OpenAlexaff
Jie Yao, Chunyu Zhang, Miquel De Cáceres, Pierre Legendre, Xiuhai Zhao

Bibliographic record

VenueJournal of Vegetation Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversité de Montréal
FundersNational Natural Science Foundation of China
KeywordsBeta diversityCommunity structureEcologyGamma diversityAssemblage (archaeology)Spatial variabilityVariation (astronomy)OrdinationSpecies diversityCommunitySpecies richnessNicheAlpha diversityDiversity (politics)GeographyBiologyHabitatStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract Questions What are the ecological processes that determine the spatial distribution of species and species diversity? Partitioning beta diversity can provide fundamental insights into the processes that determine the spatial variation of species assemblages. However, studying beta diversity is conventionally based only on species composition data, ignoring the structural component of communities. Study site Temperate mixed broadleaf–conifer forest in Jiaohe, Jilin Province, northeastern China. Methods We characterized the variation of community assemblages in terms of species composition, size structure, or considering both components. We then employed environmental and spatial variables as explanatory factors to partition the variation in both compositional and structural components of community assemblage and assess the relative contributions of the niche and neutral processes to community assembly. Results The values of overall beta diversity ( BD statistics) and the relative contribution of individual sampling units to beta diversity ( LCBD indices) depended on whether the species composition, size structure, or both together had been taken into account. The value of compositional–structural beta diversity was the largest, followed by traditional compositional beta diversity; the smallest was the structural beta diversity. The sites with high contributions to beta diversity ( LCBD values) varied among structural and compositional components. The explanatory power of the environmental variables and the spatial variables also varied widely with different components of a community. The combination of environmental and spatial variables explained the highest proportion of variation (43.8%) in the compositional component and explained the lowest proportion of variation (25.4%) in the structural component of community assemblage. Conclusion Both deterministic and stochastic processes are acting to determine community assemblages in terms of species composition and structure in our temperate forest site. Our study highlights the importance of considering the structural component of forest communities, in addition to compositional data, when studying beta 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)0.001
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 score0.366
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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