10.5061/DRYAD.2RBNZS7NM
Zhang, Chan
0000-0002-7660-042X
Henan Normal University
Li, Xianting
Henan Normal University
An, Yumeng
Henan Normal University
Zhang, Zhonghua
Chinese Academy of Sciences
Ren, Fei
Qinghai University
Zhou, Huakun
Chinese Academy of Sciences
Effects of experimental warming on vegetative and reproductive characters
of P. viviparaum in the Qinghai-Tibet Plateau
Dryad
dataset
2021
FOS: Biological sciences
National Natural Science Foundation of China
https://ror.org/01h0zpd94
31500193
National Natural Science Foundation of China
https://ror.org/01h0zpd94
2019-ZJ-908
Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Area*
2020-KF-06
The Key Program of Higher Education of Henan Province*
21A180010
Science Foundation for Young Scholars of Henan Normal University*
2019QK11
The Project of Qinghai Science and Technology Department*
2018-ZJ-967Q
National Natural Science Foundation of China
https://ror.org/01h0zpd94
31672475
Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Area
2020-KF-06
The Key Program of Higher Education of Henan Province
21A180010
Science Foundation for Young Scholars of Henan Normal University
2019QK11
The Project of Qinghai Science and Technology Department
2018-ZJ-967Q
2021-07-29T00:00:00Z
2021-07-29T00:00:00Z
en
https://doi.org/10.5281/zenodo.4904555
37713 bytes
7
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
This dataset contains data from a simulated warming experiment described
in the paper: “Zhang, C., Li, XT., and An, YM. Effects of experimental
warming on vegetative and reproductive growth of Polygonum viviparaum in
the Qinghai-Tibet Plateau. Nordic Journal of Botany. DOI:
10.1111/njb.03157”. The experiment investigated Polygonum viviparaum, a
perennial herb distributed widely in arctic and alpine regions, under two
different levels of experimental warming treatments to examine effects of
warming on its vegetative and reproductive growth. Two types of open top
chambers (OTCs), large and small, were used to generate lower and higher
warming levels. The dataset consists of the vegetative and reproductive
characters of P. viviparaum in control plots, large OTCs and small OTCs.
The characters include plant height, leaf number, length of the longest
leaf, flower diameter, bulbil length, number of flowers per spike, number
of bulbils per spike, flower proportion, dry weight of stem and
leaves, dry weight of flowers per spike, dry weight of bulbils per spike,
reproductive allocation and bulbil germination rate. Main results of the
experiment are that (1) the increased temperature promoted both vegetative
and reproductive growth of P. viviparaum, but there was a significant
trade-off between them. Decreased reproductive allocation under warming
suggested that more available resources were devoted to vegetative growth,
resulting in increased plant height, leaf number and length of the longest
leaf; (2) After warming, the number and dry weight of flowers per spike
decreased while the number and dry weight of bulbils per spike increased,
indicating more investment to asexual reproduction over sexual
reproduction in P. viviparaum; (3) The increase of warming further
strengthened the above variation trends of vegetative and reproductive
growth of P. viviparaum.
The dataset was collected during a simulated warming experiment at the
Haibei Alpine Meadow Ecosystem Research Station, Chinese Academy of
Sciences. We used the linear mixed-effect model to test the effects of
experimental warming on vegetative and reproductive characters of P.
viviparaum. In the model, ‘year’ and ‘plot’ (for bulbil germination rate,
only ‘plot’) were defined as random parameters and ‘treatment’ was defined
as fixed parameter. Differences among L, S and C treatments were analyzed
using Duncan's multiple range test. Regression analyses were used to
test the relationships between vegetative growth and reproductive growth,
vegetative growth and sexual reproduction, vegetative growth and asexual
reproduction, sexual reproduction and asexual reproduction. As the
analyses were performed on the same data, all p-values were corrected by
Bonferroni correction. Statistical analyses were performed using SAS
version 9.22 (SAS Institute, Cary, NC, USA).
The readme file contains an explanation of each of the variables in the
dataset. Information on how the measurements were done can be found in the
associated manuscript referenced above.