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Record W3120696509 · doi:10.1002/ecs2.3319

Geometric analysis of regime shifts in coral reef communities

2021· article· en· W3120696509 on OpenAlexaff
Edward W. Tekwa, Lisa C. McManus, Ariel Greiner, Madhavi A. Colton, Michael M. Webster, Malin L. Pinsky

Bibliographic record

VenueEcosphere · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of Toronto
FundersNature ConservancyGordon and Betty Moore FoundationNational Science Foundation
KeywordsCoral reefAlternative stable stateEcologyDominance (genetics)ReefIntraspecific competitionCoralHabitatCompetition (biology)EcosystemBiology

Abstract

fetched live from OpenAlex

Abstract Coral reefs are among the many communities believed to exhibit regime shifts between alternative stable states, single‐species dominance, and coexistence. Proposed drivers of regime shifts include changes in grazing, spatial clustering, and ocean temperature. Here, we distill the dynamic regimes of coral–macroalgal interaction into a three‐dimensional geometry based on stability, akin to thermodynamic phase diagrams of state transitions, to facilitate analysis. Specific regime‐shifting forces can be understood as trajectories through the cubic regime geometry. This geometric perspective allows us to understand multiple forces simultaneously in terms of the stability and persistence of interacting species. For example, in a coral–macroalgal community, grazing on macroalgae leads to alternative stable states when there is no spatial clustering (e.g., high habitat connectivity), while warming decreases coexistence. However, with spatial clustering, grazing promotes coexistence because of elevated local intraspecific competition. The geometry of regime shifts provides a general framework to analyze two‐species communities and can help conservation efforts navigate complexity and abrupt environmental changes.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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