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Record W2554753232 · doi:10.1002/ecy.1656

Non‐random food‐web assembly at habitat edges increases connectivity and functional redundancy

2016· article· en· W2554753232 on OpenAlexfundno aff
Guadalupe Peralta, Carol M. Frost, Raphaël K. Didham, Tatyana A. Rand, Jason M. Tylianakis

Bibliographic record

VenueEcology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersMarsden FundNatural Sciences and Engineering Research Council of CanadaUniversity of CanterburyRoyal Society Te ApārangiEducation New ZealandRoyal Society
KeywordsGeneralist and specialist speciesHabitatEcologyHerbivoreBiologyHabitat fragmentationFood webParasitoidFragmentation (computing)EcosystemHost (biology)

Abstract

fetched live from OpenAlex

Habitat fragmentation dramatically alters the spatial configuration of landscapes, with the creation of artificial edges affecting community structure and dynamics. Despite this, it is not known how the different food webs in adjacent habitats assemble at their boundaries. Here we demonstrate that the composition and structure of herbivore-parasitoid food webs across edges between native and plantation forests are not randomly assembled from those of the adjacent communities. Rather, elevated proportions of abundant, interaction-generalist parasitoid species at habitat edges allowed considerable interaction rewiring, which led to higher linkage density and less modular networks, with higher parasitoid functional redundancy. This was despite high overlap in host composition between edges and interiors. We also provide testable hypotheses for how food webs may assemble between habitats with lower species overlap. In an increasingly fragmented world, non-random assembly of food webs at edges may increasingly affect community dynamics at the landscape level.

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 score0.362
Threshold uncertainty score0.984

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 head0.031
GPT teacher head0.201
Teacher spread0.169 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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