Evidence map›Paper›PMID 30847071›Full record

ArticleEcology and evolution2019

Seeing is believing? Comparing plant-herbivore networks constructed by field co-occurrence and DNA barcoding methods for gaining insights into network structures.

Chunchao Zhu, Dominique Gravel, Fangliang He

Abstract read
In one paragraph

Article in Ecology and evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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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

9 citing papers in PubMed.

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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.

Chunchao ZhuState Key Laboratory of Biocontrol, School of Life Sciences Sun Yat-sen University Guangzhou China.ORCID https://orcid.org/0000-0002-9424-9987
Dominique GravelDépartement de Biologie Université de Sherbrooke Sherbrooke Quebec Canada.ORCID https://orcid.org/0000-0002-4498-7076
Fangliang HeECNU-Alberta Joint Lab for Biodiversity Study, Tiantong National Station for Forest Research, School of Ecology and Environmental Sciences East China Normal University Shanghai China.ORCID https://orcid.org/0000-0003-0774-4849

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plant-herbivore interaction networks provide information about community organization. Two methods are currently used to document pairwise interactions among plants and insect herbivores. One is the traditional method that collects plant-herbivore interaction data by field observation of insect occurrence on host plants. The other is the increasing application of newly developed molecular techniques based on DNA barcodes to the analysis of gut contents. The second method is more appealing because it documents realized interactions. To construct complete interaction networks, each technique of network construction is urgent to be assessed. We addressed this question by comparing the effectiveness and reliability of the two methods in constructing plant-Lepidoptera larval network in a 50 ha subtropical forest in China. Our results showed that the accuracy of diet identification by observation method increased with the number of observed insect occurrences on food plants. In contrast, the molecular method using three plant DNA markers were able to identify food residues for 35.6% larvae and correctly resolved 77.3% plant (diet) species. Network analysis showed molecular networks had threefold more unique host plant species but fewer links than the traditional networks had. The molecular method detected plants that were not sampled by the traditional method, for example, bamboos, bryophytes and lianas in the diets of insect herbivores. The two networks also possessed significantly different structural properties. Our study indicates the traditional observation of co-occurrence is inadequate, while molecular method can provide higher species resolution of ecological interactions.

Indexed as

diet identificationDNA barcodingmolecular networkobservation network

Identifiers

PMID30847071
PMCPMC6392357

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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.