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Record W2022250864 · doi:10.1890/07-0418.1

ON THE RELATIONSHIP BETWEEN REGIONAL AND LOCAL SPECIES RICHNESS: A TEST OF SATURATION THEORY

2008· article· en· W2022250864 on OpenAlexaff
Brian M. Starzomski, Raenelle L. Parker, Diane S. Srivastava

Bibliographic record

VenueEcology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpecies richnessEcologyBody size and species richnessMossGeographyBiology

Abstract

fetched live from OpenAlex

What are the local community consequences of changes in regional species richness and composition? To answer this question we followed the assembly of microarthropod communities in defaunated areas of moss, embedded in a larger moss "region." Regions were created by combining moss from spatially distinct sites, resulting in regional species pools that differed in both microarthropod richness and composition, but not area. Regional effects were less important than seasonality for local richness. Initial differences in regional richness had no direct effect on local species richness at any time along a successional gradient of 0.5-16 months. The structure of the regional pool affected both local richness and local composition, but these effects were seasonally dependent. Local species richness differed substantially between dates along the successional gradient and continued to increase 16 months after assembly began. To the best of our knowledge, this is the first critical test of saturation theory that experimentally manipulates regional richness. Further, our results failed to support the most important mechanisms proposed to explain the local richness-regional richness relationship. The results demonstrate that complicated interactions between assembly time, seasonality, and regional species pools contribute to structuring local species richness and composition in this community.

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)0.001
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 score0.032
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.233
Teacher spread0.202 · 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

Citations44
Published2008
Admission routes1
Has abstractyes

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