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Record W2783270634 · doi:10.1111/ele.12900

On the prevalence and dynamics of inverted trophic pyramids and otherwise top‐heavy communities

2018· review· en· W2783270634 on OpenAlexaff
Douglas J. McCauley, Gabriel Gellner, Neo D. Martinez, Richard J. Williams, Stuart A. Sandin, Fiorenza Micheli, Peter J. Mumby, Kevin S. McCann

Bibliographic record

VenueEcology Letters · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of Guelph
FundersAlfred P. Sloan FoundationNational Science Foundation
KeywordsTrophic levelEcologyBiomass (ecology)TundraNicheTop-down and bottom-up designPrimary producersBiomass partitioningArcticBiologyPhytoplanktonComputer science

Abstract

fetched live from OpenAlex

Classically, biomass partitioning across trophic levels was thought to add up to a pyramidal distribution. Numerous exceptions have, however, been noted including complete pyramidal inversions. Elevated levels of biomass top-heaviness (i.e. high consumer/resource biomass ratios) have been reported from Arctic tundra communities to Brazilian phytotelmata, and in species assemblages as diverse as those dominated by sharks and ants. We highlight two major pathways for creating top-heaviness, via: (1) endogenous channels that enhance energy transfer across trophic boundaries within a community and (2) exogenous pathways that transfer energy into communities from across spatial and temporal boundaries. Consumer-resource models and allometric trophic network models combined with niche models reveal the nature of core mechanisms for promoting top-heaviness. Outputs from these models suggest that top-heavy communities can be stable, but they also reveal sources of instability. Humans are both increasing and decreasing top-heaviness in nature with ecological consequences. Current and future research on the drivers of top-heaviness can help elucidate fundamental mechanisms that shape the architecture of ecological communities and govern energy flux within and between communities. Questions emerging from the study of top-heaviness also usefully draw attention to the incompleteness and inconsistency by which ecologists often establish definitional boundaries for communities.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations135
Published2018
Admission routes1
Has abstractyes

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