MétaCan
Menu
Back to cohort
Record W2973182908 · doi:10.1111/ele.13383

The importance of neutral over niche processes in structuring Ediacaran early animal communities

2019· letter· en· W2973182908 on OpenAlexfundaboutno aff
Emily G. Mitchell, Simon Harris, Charlotte G. Kenchington, Philip Vixseboxse, Lucy Roberts, Catherine Clark, Alexandra Dennis, Alexander Liu, Philip R. Wilby

Bibliographic record

VenueEcology Letters · 2019
Typeletter
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsnot available
FundersDepartment of Environment and Conservation, Government of Newfoundland and LabradorCambridge Philosophical SocietyNatural Environment Research CouncilSight Research UKNewnham College, University of CambridgeNatural England
KeywordsStructuringNicheEcologyNiche constructionBiologyGeography

Abstract

fetched live from OpenAlex

The relative influence of niche vs. neutral processes in ecosystem dynamics is an on-going debate, but the extent to which they structured the earliest animal communities is unknown. Some of the oldest known metazoan-dominated paleocommunities occur in Ediacaran age (~ 565 million years old) strata in Newfoundland, Canada and Charnwood Forest, UK. These comprise large and diverse populations of sessile organisms that are amenable to spatial point process analyses, enabling inference of the most likely underlying niche or neutral processes governing community structure. We mapped seven Ediacaran paleocommunities using LiDAR, photogrammetry and a laser line probe. We found that neutral processes dominate these paleocommunities, with niche processes exerting limited influence, in contrast with the niche-dominated dynamics of modern marine ecosystems. The dominance of neutral processes suggests that early metazoan diversification may not have been driven by systematic adaptations to the local environment, but instead may have resulted from stochastic demographic differences.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.901

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.013
GPT teacher head0.213
Teacher spread0.199 · 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

Citations67
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueEcology LettersSame topicPaleontology and Stratigraphy of FossilsFrench-language works237,207