Evidence map›Paper›PMID 36371593›Full record

ArticleScientific reports2022

Surprising effects of cascading higher order interactions.

Hsun-Yi Hsieh, John Vandermeer, Ivette Perfecto

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.6field-weighted citation impact, top 14% of its field
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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 2 institutions in 1 country.

Hsun-Yi HsiehGreat Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, USA. hhsieh@umich.edu.
John VandermeerDepartment of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA.
Ivette PerfectoSchool for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA.
University of Michigan–Ann Arbor · USGreat Lakes Bioenergy Research Center · US

Funding

National Science Foundation NSF- DEB-0349388
6 · The paper itself

Abstract

Most species are embedded in multi-interaction networks. Consequently, theories focusing on simple pair-wise interactions cannot predict ecological and/or evolutionary outcomes. This study explores how cascading higher-order interactions (HOIs) would affect the population dynamics of a focal species. Employing a system that involves a myrmecophylic beetle, a parasitic wasp that attacks the beetle, an ant, and a parasitic fly that attacks the ant, the study explores how none, one, and two HOIs affect the parasitism and the sex ratio of the beetle. We conducted mesocosm experiments to examine these HOIs on beetle survival and sex ratio and found that the 1st degree HOI does not change the beetle's survival rate or sex ratio. However, the 2nd degree HOI significantly reduces the beetle's survival rate and changes its sex ratio from even to strongly female-biased. We applied Bayes' theorem to analyze the per capita survival probability of female vs. male beetles and suggested that the unexpected results might arise from complex eco-evolutionary dynamics involved with the 1st and 2nd degree HOIs. Field data suggested the HOIs significantly regulate the sex ratio of the beetle. As the same structure of HOIs appears in other systems, we believe the complexity associated with the 2nd degree HOI would be more common than known and deserve more scientific attention.

Indexed as

ColeopteraWaspsAnimalsBayes TheoremFemaleMalePopulation Dynamics

Identifiers

PMID36371593
PMCPMC9653485
OpenAlexW4309077229

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.