Evidence map›Paper›PMID 40475866›Full record

ArticleEcology and evolution2025

Assessment of a Social Media-Based Method for Determining Raptor Diet.

Leo Biggs, Greg S Baxter, Stephen J S Debus, Neal Finch, Anysha Riggs, Hayden Houweling, Melissa Appleby, Peter J Murray

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Leo BiggsSchool of Agriculture and Environmental Science University of Southern Queensland Toowoomba Queensland Australia.ORCID https://orcid.org/0000-0002-7674-2903
Greg S BaxterSchool of Agriculture and Environmental Science University of Southern Queensland Toowoomba Queensland Australia.
Stephen J S DebusZoology, School of Environmental and Rural Science University of New England Armidale New South Wales Australia.
Neal FinchSchool of Agriculture and Environmental Science University of Southern Queensland Toowoomba Queensland Australia.
Anysha RiggsSchool of Agriculture and Environmental Science University of Southern Queensland Toowoomba Queensland Australia.
Hayden HouwelingSchool of Agriculture and Environmental Science University of Southern Queensland Toowoomba Queensland Australia.
Melissa ApplebySchool of Agriculture and Environmental Science University of Southern Queensland Toowoomba Queensland Australia.ORCID https://orcid.org/0009-0009-3734-0889
Peter J MurraySchool of Agriculture and Environmental Science University of Southern Queensland Toowoomba Queensland Australia.ORCID https://orcid.org/0000-0003-1143-1706

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate dietary information is important to improve ecological knowledge and assist in the conservation of threatened predators and their prey. Globally, many raptor populations are threatened and would benefit from accurate diet information. However, the existing methods of collecting diets can be time consuming, biased, or unfeasible for large spatial areas. Use of citizen science has been suggested to address these issues, but critical analysis of the method and comparison with existing methods is largely incomplete. Here, we evaluate the accuracy, efficiency, and biases of raptor diets data mined from social media. Two Australian coastal raptor species, Eastern Osprey (

Indexed as

Bird of preycitizen sciencedietmethod efficiencypredatorpreyraptorsocial media

Identifiers

PMID40475866
PMCPMC12137024

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.