Evidence map›Paper›PMID 40805062›Full record

ArticleAnimals : an open access journal from MDPI2025

Identification of Keystone Plant Species for Avian Foraging and Nesting in Beijing's Forest Ecosystems: Implications for Urban Forest Bird Conservation.

Lele Lin, Yongjian Zhao, Chao Yuan, Yushu Zhang, Siyu Qiu, Jixin Cao

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2025. 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
–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

0 citing papers in PubMed.

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

6 authors.

Lele LinEcology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing 100091, China.ORCID 0009-0009-1070-147X
Yongjian ZhaoBeijing Xishan Experiment Forest Center, Beijing 100093, China.
Chao YuanBeijing Key Laboratory of Ecological Function Assessment and Regulation Technology of Green Space, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China.
Yushu ZhangBeijing Key Laboratory of Ecological Function Assessment and Regulation Technology of Green Space, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China.
Siyu QiuBeijing Xishan Experiment Forest Center, Beijing 100093, China.ORCID 0000-0003-2497-5331
Jixin CaoBeijing Key Laboratory of Ecological Function Assessment and Regulation Technology of Green Space, Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China.

Funding

Beijing Youth Innovative Talent Support and Innovation Special Project for Landscaping and Greening KJCX202301
6 · The paper itself

Abstract

Urban wildlife conservation is emerging as a critical component of sustainable city ecosystems. Rather than simply increasing tree abundance or species richness, conservation management should focus on key species. In this research, Xishan Forest Park in Beijing was chosen as a case study. Our aim was to identify keystone taxa critical for avian foraging and nesting during the breeding season. We performed a network analysis linking bird species, their diets, and nest plants. Dietary components were detected using DNA metabarcoding conducted with avian fecal samples. Nest plants were identified via transect surveys. Two indices of the network, degree and weighted mean degree, were calculated to evaluate the importance of the dietary and nest plant species. We identified 13 bird host species from 107 fecal samples and 14 bird species from 107 nest observations. Based on the degree indices, fruit trees

Indexed as

bird conservationkeystone speciesurban forest

Identifiers

PMID40805062
PMCPMC12345470

What OpenQuestion holds

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LicenceCC BY
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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.