Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 583

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Size, foraging, and food web structure

Open access
Petchey, Owen L., Beckerman, Andrew P., Riede, Jens O., Warren, Philip H.·2008·Proceedings of the National Academy of Sciences

Abstract

Understanding what structures ecological communities is vital to answering questions about extinctions, environmental change, trophic cascades, and ecosystem functioning. Optimal foraging theory was conceived to increase such understanding by providing a framework with which to predict species interactions and resulting community structure. Here, we use an optimal foraging model and allometries of foraging variables to predict the structure of real food webs. The qualitative structure of the resulting model provides a more mechanistic basis for the phenomenological rules of previous models. Quantitative analyses show that the model predicts up to 65% of the links in real food webs. The deterministic nature of the model allows analysis of the model's successes and failures in predicting particular interactions. Predacious and herbivorous feeding interactions are better predicted than pathogenic, parasitoid, and parasitic interactions. Results also indicate that accurate prediction and modeling of some food webs will require incorporating traits other than body size and diet choice models specific to different types of feeding interaction. The model results support the hypothesis that individual behavior, subject to natural selection, determines individual diets and that food web structure is the sum of these individual decisions.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.0710672105

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

Metadata

Authors
Petchey, O. L., Beckerman, A. P., Riede, J. O., & Warren, P. H.
Venue
Proceedings of the National Academy of Sciences
Published
2008-03-18
Volume
105
Issue
11
Pages
4191-4196
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2105283073
Cited by
583
References
4
FWCI
41.49
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.