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Record W4289754922 · doi:10.1111/conl.12914

Woody plant phylogenetic diversity supports nature's contributions to people but is at risk from human population growth

2022· article· en· W4289754922 on OpenAlexaff
T. Jonathan Davies, Olivier Maurin, Kowiyou Yessoufou, Barnabas H. Daru, Bezeng S. Bezeng, Ledile T. Mankga, Hanno Schaefer, Wilfried Thuiller, Michelle van der Bank

Bibliographic record

VenueConservation Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiodiversityPhylogenetic treeEcologyPhylogenetic diversitySpecies richnessDiversity (politics)EcosystemBiologyPopulationWoody plantPhylogeneticsGeographyAgroforestryDemography

Abstract

fetched live from OpenAlex

Abstract The Tree of Life—phylogeny—provides a powerful tool for understanding the processes regulating life's diversity. Conserving the branches on the Tree of Life might also have practical benefits. Using a comprehensive phylogeny of southern African woody trees and shrubs, and structural equation modeling, we show that human population density correlates closely with not only the richness of woody plants in a region but also their evolutionary relatedness. Further, we demonstrate that more phylogenetically diverse species assemblages support a greater diversity of ecosystem goods and services. Our results suggest that people in Africa may gain material benefits from inhabiting regions that support high phylogenetic diversity of woody plants. However, the correlation between human population and woody plant diversity creates a tension between people and biodiversity, which could threaten the contributions to people provided by intact and phylogenetically diverse 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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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.

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

Citations9
Published2022
Admission routes1
Has abstractyes

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