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Record W2984724136 · doi:10.1111/1365-2656.13136

Urbanization reshapes a food web

2019· article· en· W2984724136 on OpenAlexaff
Denon Start, Matthew A. Barbour, Colin Bonner

Bibliographic record

VenueJournal of Animal Ecology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUrbanizationBiodiversityFood webEcologyGeographyUrban ecosystemEcosystem servicesEconomic geographyEcosystemBiology

Abstract

fetched live from OpenAlex

Cities represent humanity's most intense impact on our planet, with more than half of all humans now residing in urban areas. Indeed, urbanization has well-understood impacts on both individual species and general patterns of biodiversity. However, species do not exist in isolation, but are instead members of complex interaction networks that shape patterns of diversity and influence ecosystem services. Despite the importance of species interaction for creating patterns of diversity, we do not understand how urbanization alters these interactions. Here, we investigate how an interaction network (food web) is reshaped by urbanization. We show that, consistent with theory, cities tend to support less diverse ecological communities, and rare species that interact with few species are particularly sensitive to urbanization. As a result, remnant urban food webs tend to have more interactions per species and greater connectance, creating more integrated interaction networks. We discuss the implications of this food web reshaping for ecological stability, eco-evolutionary dynamics, and the joining of interaction networks and conservation planning. The role of cities in reshaping interaction networks provides an interesting study of food web (dis)assembly, while also shedding light on new approaches to applied conservation issues.

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 score0.702
Threshold uncertainty score0.744

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)0.0010.000

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 head0.028
GPT teacher head0.205
Teacher spread0.177 · 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

Citations54
Published2019
Admission routes1
Has abstractyes

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