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Record W2991201201 · doi:10.1126/sciadv.aaz0414

The commonness of rarity: Global and future distribution of rarity across land plants

2019· article· en· W2991201201 on OpenAlexfundno aff
Brian J. Enquist, Xiao Feng, Brad Boyle, Brian Maitner, Erica A. Newman, Peter M. Jørgensen, Patrick R. Roehrdanz, Barbara M. Thiers, Joseph R. Burger, Richard T. Corlett, Thomas L. P. Couvreur, Gilles Dauby, John C. Donoghue, Wendy Foden, Jon C. Lovett, Pablo A. Marquet, Cory Merow, Guy F. Midgley, Naia Morueta‐Holme, Danilo M. Neves, Ary Teixeira de Oliveira‐Filho, Nathan J. B. Kraft, Daniel Park, Robert K. Peet, Michiel Pillet, Josep M. Serra‐Diaz, Brody Sandel, Mark Schildhauer, Irena Šímová, Cyrille Violle, Jan J. Wieringa, Susan K. Wiser, Lee Hannah, Jens‐Christian Svenning, Brian J. McGill

Bibliographic record

VenueScience Advances · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
FundersDivision of Emerging FrontiersUniversity of California, Santa BarbaraUniversidade Federal do MaranhãoAarhus Universitets ForskningsfondUniversidad Autónoma de YucatánUniversity of Prince Edward IslandUniversidade Federal do Rio Grande do NorteUniversity of Texas at El PasoUniversidade Federal do Rio de JaneiroMinistry of Business, Innovation and EmploymentUniversidad de ExtremaduraFondation pour la Recherche sur la BiodiversiteNational Research FoundationResearch Institute for Oceanochemistry FoundationSan José State UniversityUniverzita Karlova v PrazeScheme for Promotion of Academic and Research CollaborationGlobal Environment FacilityNew Mexico State UniversityUniversidad Pública de NavarraUniversidad Nacional de San LuisCenter for Makroøkologi, Evolution og KlimaBentham-Moxon TrustVillum FondenUniversity of VictoriaDivision of Biological InfrastructureDanmarks GrundforskningsfondUniversity of ArizonaUniversidad Juárez Autónoma de TabascoEuropean CommissionUniversidade Estadual de Santa CruzAarhus UniversitetNational Science Foundation
KeywordsDistribution (mathematics)GeographyEcologyEnvironmental resource managementBiologyEnvironmental science

Abstract

fetched live from OpenAlex

A key feature of life's diversity is that some species are common but many more are rare. Nonetheless, at global scales, we do not know what fraction of biodiversity consists of rare species. Here, we present the largest compilation of global plant diversity to quantify the fraction of Earth's plant biodiversity that are rare. A large fraction, ~36.5% of Earth's ~435,000 plant species, are exceedingly rare. Sampling biases and prominent models, such as neutral theory and the k-niche model, cannot account for the observed prevalence of rarity. Our results indicate that (i) climatically more stable regions have harbored rare species and hence a large fraction of Earth's plant species via reduced extinction risk but that (ii) climate change and human land use are now disproportionately impacting rare species. Estimates of global species abundance distributions have important implications for risk assessments and conservation planning in this era of rapid global change.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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

Citations337
Published2019
Admission routes1
Has abstractyes

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