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Record W4387931916 · doi:10.1002/ecy.4189

Root‐centric β diversity reveals functional homogeneity while phylogenetic heterogeneity in a subtropical forest

2023· article· en· W4387931916 on OpenAlexaff
Wenqi Luo, Youshi Wang, James F. Cahill, Fucheng Luan, Yonglin Zhong, Yuanzhi Li, Buhang Li, Chengjin Chu

Bibliographic record

VenueEcology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of Alberta
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceChina Postdoctoral Science FoundationPostdoctoral Research Foundation of ChinaNational Natural Science Foundation of China
KeywordsBiologyBiological dispersalBeta diversityPhylogenetic treeEcologyPhylogenetic diversityGamma diversityTropical and subtropical moist broadleaf forestsEcosystemPhylogeneticsAbiotic componentNestednessSubtropicsBiodiversity

Abstract

fetched live from OpenAlex

Abstract Root‐centric studies have revealed fast taxonomic turnover across root neighborhoods, but how such turnover is accompanied by changes in species functions and phylogeny (i.e., β diversity) remains largely unknown. As β diversity can reflect the degree of community‐wide biotic homogenization, such information is crucial for better inference of below‐ground assembly rules, community structuring, and ecosystem processes. We collected 2480 root segments from 625 0–30 cm soil profiles in a subtropical forest in China. Root segments were identified into 138 species with DNA‐barcoding with six root morphological and architectural traits measured per species. By using the mean pairwise (Dpw) and mean nearest neighbor distance (Dnn) to quantify species ecological differences, we first tested the non‐random functional and phylogenetic turnover of root neighborhoods that would lend more support to deterministic over stochastic community assembly processes. Additionally, we examined the distance‐decay pattern of β diversity, and finally partitioned β diversity into geographical and environmental components to infer their potential drivers of environmental filtering, dispersal limitation, and biotic interactions. We found that functional turnover was often lower than expected given the taxonomic turnover, whereas phylogenetic turnover was often higher than expected. Phylogenetic Dpw (e.g., interfamily species) turnover exhibited a distance‐decay pattern, likely reflecting limited dispersal or abiotic filtering that leads to the spatial aggregation of specific plant lineages. Conversely, both functional and phylogenetic Dnn (e.g., intrageneric species) exhibited an inverted distance‐decay pattern, likely reflecting strong biotic interactions among spatially and phylogenetically close species leading to phylogenetic and functional divergence. While the spatial distance was generally a better predictor of β diversity than environmental distance, the joint effect of environmental and spatial distance usually overrode their respective pure effects. These findings suggest that root neighborhood functional homogeneity may somewhat increase forest resilience after disturbance by exhibiting an insurance effect. Likewise, root neighborhood phylogenetic heterogeneity may enhance plant fitness by hindering the transmission of host‐specific pathogens through root networks or by promoting interspecific niche complementarity not captured by species functions. Our study highlights the potential role of root‐centric β diversity in mediating community structures and functions largely ignored in previous studies.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.027
GPT teacher head0.218
Teacher spread0.190 · 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 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

Citations10
Published2023
Admission routes1
Has abstractyes

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