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How is diversity related to species turnover through time?

2007· article· en· W2050144321 on OpenAlexaff
Jonathan B. Shurin

Bibliographic record

VenueOikos · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpecies richnessEcologyExtinction (optical mineralogy)Abiotic componentBiologyNicheRange (aeronautics)Species diversityTurnoverBody size and species richnessColonizationBeta diversity

Abstract

fetched live from OpenAlex

Empirical studies across a wide range of taxa show that the slopes of species–time relationships often decline as average species richness increases, indicating that more diverse communities have greater temporal stability in species composition. I explored potential explanations for this observation using two simple model formulations for species temporal dynamics. In the Abiotic model, species turnover is governed by the degree of heterogeneity in the environment and the range of species’ tolerances. In this case, more variable conditions lead to lower species richness and higher turnover, but only if the distribution of species’ niche widths and the size of the species pool are independent of the degree of environmental variability. The Biotic model represents direct effects of diversity on turnover through positive or negative feedbacks between diversity and species’ colonization and extinction rates. Declining turnover with increasing richness occurred when higher diversity either facilitated colonization by new species or reduced extinction rates of extant species. Both models could produce the observed pattern of declining turnover at higher diversity under some circumstances, however the conditions for this outcome in the Abiotic model were restrictive and potentially unrealistic. The models provide a process‐based framework for understanding the connection between diversity and species turnover through time.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

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

Citations80
Published2007
Admission routes1
Has abstractyes

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