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Record W3106470443 · doi:10.1126/sciadv.abc5439

Outsized effect of predation: Wolves alter wetland creation and recolonization by killing ecosystem engineers

2020· article· en· W3106470443 on OpenAlexaff
Thomas D. Gable, Sean Johnson‐Bice, Austin T. Homkes, Steve K. Windels, Joseph K. Bump

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsUniversity of Manitoba
FundersNational Science Foundation
KeywordsBeaverPredationEcosystemWetlandEcosystem engineerTrophic cascadeEcological successionEcologyTrophic levelBiologyFood web

Abstract

fetched live from OpenAlex

Gray wolves are a premier example of how predators can transform ecosystems through trophic cascades. However, whether wolves change ecosystems as drastically as previously suggested has been increasingly questioned. We demonstrate how wolves alter wetland creation and recolonization by killing dispersing beavers. Beavers are ecosystem engineers that generate most wetland creation throughout boreal ecosystems. By studying beaver pond creation and recolonization patterns coupled with wolf predation on beavers, we determined that 84% of newly created and recolonized beaver ponds remained occupied until the fall, whereas 0% of newly created and recolonized ponds remained active after a wolf killed the dispersing beaver that colonized that pond. By affecting where and when beavers engineer ecosystems, wolves alter all of the ecological processes (e.g., water storage, nutrient cycling, and forest succession) that occur due to beaver-created impoundments. Our study demonstrates how predators have an outsized effect on ecosystems when they kill ecosystem engineers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.176

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 communication0.0000.001
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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

Citations70
Published2020
Admission routes1
Has abstractyes

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