DOI: 10.5061/dryad.z34tmpgpr
title: Warming reduces mid-summer flowering plant reproductive success through advancing fruiting phenology in an alpine meadow

description: Changes in reproductive phenology induced by warming are happening across
 the globe with significant implications for plant sexual reproduction,
 however, the response of plant reproductive efforts (number of flowers and
 fruits) and success (successful fruits/total flowers) in response to
 climate change have not been well-characterized. Here, we conducted a
 warming and altered precipitation experiment in an alpine meadow on the
 eastern Tibetan Plateau to investigate the effects of climate change on
 the reproductive phenology and success of six common species belonging to
 two flowering functional groups (FFGs). We found that warming advanced the
 start of flowering both FFGs and the start of fruiting in mid-summer
 flowering (MSF) plants. Warming reduced the reproductive efforts of
 early-spring flowering (ESF) plants but did not change their reproductive
 success, while the effects of warming and altered precipitation on the
 reproductive efforts and success of MSF plants were year-dependent, and
 the fruiting phenology regulated the response of the MSF plant’s
 reproductive success to climate change. The findings highlight the
 critical role of fruiting phenology in the reproductive success of alpine
 plants and imply that alpine plants may reduce their fitness by producing
 fewer flowers and fruits under climate warming, especially for later
 flowering plants.
description: <strong>2.3 Soil temperature and moisture
 measurements</strong> We monitored the soil
 temperature and moisture of each plot during the growing season from April
 to October in 2021 and 2022. EM50 Data collection System (Decagon Devices,
 Inc., USA) was used to monitor soil temperature and moisture at 5 cm soil
 depth [36]. Data were sampled at 1-minute intervals, and then the
 15-minute average was automatically stored in the logger. We chose soil
 temperature instead of air temperature because previous studies showed
 that soil temperature was a better predictor of alpine plant phenology
 than air temperature [38]. <strong>2.4 Phenology
 monitoring</strong> Dominance and flowering
 frequency of common plants were used as criteria to select for
 phenological and reproduction monitoring [36, 39]. In late August (when
 the cumulative biomass of plant communities reached the maximum) from 2019
 to 2020, one 50 × 50 cm quadrat was randomly selected at least 0.5 m from
 the edge of each plot. We harvested all the stems and leaves in each
 quadrat, sorted them into species, dried them to a constant weight at 70℃,
 and then weighed them to 0.001g to estimate the aboveground biomass of
 individual species between treatments. We counted the relative biomass and
 abundance of individual species. In the fifth and sixth years of the
 warming and precipitation change treatments (2021 and 2022), we selected
 the six common species with a high frequency of sexual reproduction in
 communities for phenological and reproduction observation to enable enough
 plant material for trait collection and ensure that these species
 represent well the studied communities (Table 1). The selected pool of six
 species made up 60-70% of the relative cover, 70% of the total biomass of
 the plant community, and represented common flowering functional types
 found in the alpine meadow on the eastern Tibetan Plateau [29, 38,
 39]. The monitored species were classified into two
 flowering functional groups: early-spring flowering species (<em>K.
 graminifolia</em> and<em> A. rivularis</em>) that
 flowered before the beginning of June and mid-summer flowering species
 (<em>A. obtusiloba</em>, <em>Pleurospermum
 camtschaticum</em>, <em>E. nutans</em> and<em> P.
 pratensis</em>) that flowered between the beginning of June and July
 [30, 38]. As soon as each of the six focus species produces obvious flower
 buds or flowering stems (for grasses), five individuals of each species in
 each plot will be marked with color-coded tags (five individuals that lost
 tags during the study were replaced by similar-sized individuals). The
 marked individuals were monitored during the growing season at intervals
 of 5-7 days [4, 57]. We recorded the first flower budding day (hereafter
 referred to as “FFBD”); the first flowering day (hereafter referred to as
 “FFD”); and the first fruit setting day (hereafter referred to as “FFSD”).
 The phenological duration was calculated as the number of days between the
 onset and conclusion of each phenological event [23, 47].
 <strong>2.5 Reproductive output and
 success</strong> At each phenological census
 through the growing season, we also counted the total number of open
 flowers and fruits on all marked individuals of each species. For grasses,
 we recorded the number of racemes or inflorescences [58]. Individual,
 graminoid species, were defined as genets of visibly integrated ramets
 [57], and where possible rhizomatous species were partially exposed to
 determine genet size [59]. For clarity, we hereafter use ‘flower’ and
 ‘fruit’ for all species. With these variables, reproductive output was
 estimated as the number of flowers and fruits produced by each marked
 individual, and reproductive success was estimated as the proportion of
 flowers on each plant that successfully produced fruits/seeds [7, 43].
 Notably, <em>P. pratensis</em> is a typical
 single-inflorescence plant, only 0 and 1 were obtained according to the
 reproductive success calculation method in this study. Thus, only five of
 the six focus species were counted for reproductive output and success.
 All reproductive phenological events and reproductive indicators were
 averaged for the five individuals of each species within each plot for
 further analysis [57, 60].
description: # Warming reduces mid-summer flowering plant reproductive success through
 advancing fruiting phenology in an alpine meadow
 [https://doi.org/10.5061/dryad.z34tmpgpr](https://doi.org/10.5061/dryad.z34tmpgpr) ## Description of the data and file structure Changes in reproductive phenology induced by warming are happening across the globe with significant implications for plant sexual reproduction. However, the response of plant reproductive output (number of flowers and fruits) and success (successful fruits/total flowers) in response to climate change have not been well-characterized. Here, we conducted a warming and altered precipitation experiment in an alpine meadow on the eastern Tibetan Plateau to investigate the effects of climate change on the reproductive phenology and success of six common species belonging to two flowering functional groups. We found that warming advanced the start of flowering and the start of fruiting in mid-summer flowering plants. Warming reduced the reproductive output of early-spring flowering plants but did not change their reproductive success. The effects of warming and altered precipitation on the reproductive output and success of mid-summer flowering plants were year-dependent, and the fruiting phenology regulated the response of the mid-summer flowering plant’s reproductive success to climate change. The findings highlight the critical role of fruiting phenology in the reproductive success of alpine plants and imply that alpine plants may reduce their fitness by producing fewer flowers and fruits under climate warming, especially for later flowering plants. | **Attribute** | **Description** | **Category/units** | | :------------------------- | :-------------------------------------------------------------------------------------------------------------------- | :----------------- | | **ID** | The number of all dateset | | | **FFG** | Flowering functional groups (early-spring flowering, ESF; mid-summer flowering, MSF) | | | **Treatment** | Six treatment level (ambient, CK; warming, W; wet, PI; drought, PR; warming plus wet, WPI, warming plus drought, WPR) | | | **Block** | Randomized block design with warming and altered precipitation | | | **Warming** | Warming, 1; no warming, 0 | | | **Precipitation\_changes** | Ambient precipitation, 0; drought, -1; wet, 1 | | | **Soil\_temperature** | Soil temprature | °C | | **Soil\_moisture** | Soil moisture | v/v | | **FFBD** | First flower budding date | day | | **FFD** | First flowering date | day | | **FFSD** | First fruit setting date | day | | **FBD** | Flower budding duration | days | | **FD** | Flowering duration | days | | **Num.flowers** | Number of flowers | | | **Num.fruits** | Number of fruits | | | **Reproductive\_success** | The proportion of flowers on each plant that successfully produced fruits/seeds | | ## Code/software Linear mixed-effects (LME) models were used to assess the effects of year, warming, and precipitation changes on soil temperature and moisture, in which year, warming, precipitation changes, and their interactions were treated as fixed factors, while the block was treated as a random factor. To investigate the effects of warming and precipitation changes on the phenology and reproductive characteristics of different flowering functional groups, we also employed the LME models to assess the effects of year, warming, precipitation changes, flowering functional groups, and their interactions on key phenological events (including reproductive phenology and phenological duration), as well as on reproductive output and success. Here, year, warming, precipitation, flowering functional groups, and their interactions were treated as fixed factors, while the block was treated as a random factor. We chose a linear mixed effects model considering the year according to the Akaike Information Criterion (AIC) (Appendix S1: Tables S10). Furthermore, given the significant annual climatic fluctuations in the region [29, 38, 49] and the fact that we did not monitor soil temperature and moisture during the non-growing season, we did not consider the effects of soil temperature, soil moisture, and their annual change rates on alpine plant phenology and reproduction. One-way ANOVA followed by Tukey's HSD tests was used for multiple comparisons to detect differences in reproductive phenology across different years among the six treatment levels of the two flowering functional groups. We applied a similar statistical strategy using LME models when analyzing the reproductive output and success of the two flowering functional groups, in which warming, precipitation, and their interaction were treated as fixed factors, and the block was treated as a random factor. Differences were defined as significant when *p* &lt; 0.05. Linear regression models were constructed to analyze the relationships between phenological events/reproductive output and success, and the changes in soil temperature and moisture, focusing on species and flowering functional groups across all plots, to evaluate the response of reproductive phenology to variations in soil temperature and moisture.  To investigate whether the responses of reproductive phenology of alpine plants to climate change regulate their reproductive output and success, we conducted the following three analyses. First, Pearson’s correlation analysis was used to analyze the correlation between reproductive phenology and reproductive output across different flowering functional groups. Second, residuals from the regressions of reproductive phenology events or reproductive output on the soil temperature were used to test for independent effects of changes in phenology on the reproductive output. Third, piecewise structural equation modeling (SEM) was utilized to analyze the main factors influencing alpine plant reproductive success under warming and precipitation changes, as well as their underlying mechanisms. We initially formulated an a priori model encompassing all hypothesized pathways (Appendix S1: Figure S9). Furthermore, given the disparities in temperature sensitivity and drought tolerance exhibited by different flowering functional groups [29, 30, 38, 47], the impending impacts of warming and precipitation changes on their reproductive phenology and reproduction are likely to diverge significantly. Thus, the data used in the SEM were collected from each plot across two years and we ran the model separately for each flowering functional group (early-spring flowering plants and mid-summer flowering plants). We assessed model fit by d-separation tests and ![](file:///C:/Users/Lenovo/AppData/Local/Temp/msohtmlclip1/01/clip_image002.png) test. Finally, we used Fisher’s statistics and *P* value to evaluate the fit of the model [61]. All the statistical analyses were conducted in R version 4.3.0 (R Development Core Team, 2022), with the ‘lme’ function from the ‘nlme’ package for all linear mixed-effects models. All Tukey’s HSD tests for multiple comparisons of differences use the ‘TukeyHSD’ function in the ‘multcomp’ package. The ‘psem’ function in ‘piecewiseSEM’ package for SEM [62].