Evidence map›Paper›PMID 38627532›Full record

ArticleScientific reports2024

Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes.

Qianfeng Wang, Huixia Chen, Feng Xu, Virgílio A Bento, Rongrong Zhang, Xiaoping Wu, Pengcheng Guo

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. 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
5.5field-weighted citation impact, top 4% 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, 12 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

7 authors at 4 institutions in 2 countries.

Qianfeng WangCollege of Environmental and Safety Engineering/The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, China. wangqianfeng@fzu.edu.cn.
Huixia ChenCollege of Environmental and Safety Engineering/The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, China.
Feng XuCollege of Environmental and Safety Engineering/The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, China.
Virgílio A BentoFaculdade de Ciências, Instituto Dom Luiz, Universidade de Lisboa, 1749-016, Lisboa, Portugal.
Rongrong ZhangCollege of Environmental and Safety Engineering/The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, China.
Xiaoping WuCollege of Environmental and Safety Engineering/The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350116, China.
Pengcheng GuoSchool of Ecology and Environment, Hainan University, Haikou, 570228, China. guopengcheng08@mails.ucas.ac.cn.
Fuzhou University · CNHainan University · CNMinistry of Education of the People's Republic of China · CNUniversity of Lisbon · PT

Funding

Natural Science Foundation of Fujian Province 2021J01627Natural Science Foundation of Hainan Province 623QY621
6 · The paper itself

Abstract

Previous studies have primarily focused on the influence of temperature and precipitation on phenology. It is unclear if the easily ignored climate factors with drivers of vegetation growth can effect on vegetation phenology. In this research, we conducted an analysis of the start (SOS) and end (EOS) of the growing seasons in the northern region of China above 30°N from 1982 to 2014, focusing on two-season vegetation phenology. We examined the response of vegetation phenology of different vegetation types to preseason climatic factors, including relative humidity (RH), shortwave radiation (SR), maximum temperature (Tmax), and minimum temperature (Tmin). Our findings reveal that the optimal preseason influencing vegetation phenology length fell within the range of 0-60 days in most areas. Specifically, SOS exhibited a significant negative correlation with Tmax and Tmin in 44.15% and 42.25% of the areas, respectively, while EOS displayed a significant negative correlation with SR in 49.03% of the areas. Additionally, we identified that RH emerged as the dominant climatic factor influencing the phenology of savanna (SA), whereas temperature strongly controlled the SOS of deciduous needleleaf forest (DNF) and deciduous broadleaf forest (DBF). Meanwhile, the EOS of DNF was primarily influenced by Tmax. In conclusion, this study provides valuable insights into how various vegetation types adapt to climate change, offering a scientific basis for implementing effective vegetation adaptation measures.

Indexed as

ForestsPlant DevelopmentChinaClimate ChangeEcosystemSeasonsTemperatureClimate changePhenologyPreseasonVegetation

Identifiers

PMID38627532
PMCPMC11021431
OpenAlexW4394844982

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.