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Record W2583294912 · doi:10.1111/oik.03517

Refuge increases food chain length: modeled impacts of littoral structure in lake food webs

2017· article· en· W2583294912 on OpenAlexaff
Jacob P. Ziegler, Irene Gregory‐Eaves, Christopher T. Solomon

Bibliographic record

VenueOikos · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsFood chainTrophic levelEcosystemFood webLittoral zoneEcologyEnvironmental scienceBiomass (ecology)Apex predatorLake ecosystemBiology

Abstract

fetched live from OpenAlex

Food chain length (FCL) represents a fundamental metric within ecology because it has implications for ecosystem function and responses to environmental change. Omnivory between linked food chains situated within large ecosystems can increase FCL, whereas overlap of food chains within small or spatially compressed ecosystems is generally thought to decrease FCL. Yet FCL varies widely in small ecosystems and the mechanisms underlying determinants of FCL in these systems is unclear. In small shallow lakes, littoral structure is a predictor of FCL but it is unclear whether this is due to productivity or refuge mechanisms. Here we provide evidence, using consumer resource food web modules parameterized with empirical data, that refuge in spatially compressed ecosystems has the ability on its own to increase the trophic position of top predators by increasing the biomass of top and intermediate predators across a range of common food web module structures. Our results suggest that refuge is an important driver of FCL in small ecosystems, which has implications for determining responses of these systems to environmental change.

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 score0.128
Threshold uncertainty score0.888

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 communication0.0000.000
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 head0.012
GPT teacher head0.231
Teacher spread0.218 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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