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Record W2904051129 · doi:10.1111/ele.13185

The strength of ecological subsidies across ecosystems: a latitudinal gradient of direct and indirect impacts on food webs

2018· review· en· W2904051129 on OpenAlexafffund
Laurent Montagano, Shawn Leroux, Marie‐Andrée Giroux, Nicolas Lecomte

Bibliographic record

VenueEcology Letters · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsMemorial University of NewfoundlandCenter for Northern StudiesUniversité de Moncton
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNew Brunswick Innovation FoundationCanada Foundation for InnovationUniversité de Moncton
KeywordsEcosystemEcologyBiomeTrophic levelTrophic cascadeTemperate climateEnvironmental scienceAbundance (ecology)Food webBiology

Abstract

fetched live from OpenAlex

Material and energy flows among ecosystems can directly and indirectly drive ecosystem functions. Yet, how populations of consumers respond to allochthonous inputs at a macroecological scale is still unclear. Using a meta-analysis spanning several biomes, we show that the abundance of recipient populations is 36-57% larger with increased allochthonous inputs. The strength of direct effects on the recipients of these inputs as well as the indirect effects on the consumers of these recipients (i.e. ascending indirect effects) are constant across a latitudinal gradient spanning subtropical, arid, temperate, boreal and arctic ecosystems. However, indirect effect on the in situ resources of the input recipient (i.e. descending indirect effects) decreases with latitude. Our results suggest that the influence of allochthonous inputs can vary across large-scale gradients of ecosystem productivity and may be driven by the types of trophic interactions within recipient food webs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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

Citations39
Published2018
Admission routes2
Has abstractyes

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