This Wang_2022_dataset of manusicript readme.txt file was generated on 2022-5-13 by Wang et al. GENERAL INFORMATION 1. Title of Dataset: Data from: Architectural effects regulate resource allocation within the inflorescences with nonlinear blooming patterns (American Journal of Botany) 2. Author Information A. Principal Investigator Contact Information Name: Hao Wang Institution: Yunnan University Address: Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology, Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, 650504, Yunnan, China Email: 6270888466@qq.com B. Associate or Co-investigator Contact Information Name: Zhi-Qiang Zhang Institution: Yunnan University Address: Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology, Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, 650504, Yunnan, China Email: zq.zhang@ynu.edu.cn C. Alternate Contact Information Name: Wen Guo Institution: Yunnan University Address: Yunnan Key Laboratory of Plant Reproductive Adaptation and Evolutionary Ecology, Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming, 650504, Yunnan, China Email: 1013149327@qq.com 3. Date of data collection (single date, range, approximate date) : The date of data collection was from 2015 to 2021. 4. Geographic location of data collection : All the data of this study were collected in a natural population near the Shangri-La Alpine Botanical Garden, Yunnan Province, SW China (27°54′N, 99°38′E, 3300-3350 m a.s.l) 5. Information about funding sources that supported the collection of the data: The collection of the data was funded by the National Natural Science Foundation of China (no. 31760104 to ZQZ; and no. 31960349 to BZ) SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: No Licenses or restrictions 2. Links to publications that cite or use the data: We don't have a link yet 3. Links to other publicly accessible locations of the data: None 4. Links/relationships to ancillary data sets: None 5. Was data derived from another source? yes/no No 6. Recommended citation for this dataset: None DATA & FILE OVERVIEW 1. File List: File 1 Name: Wang_2022_dataset of manusicript.xls File 1 Description: data collected on Shangri-La, including fruit trait, flower longevity, reproductive success, etc. 2. Relationship between files, if important: None 3. Additional related data collected that was not included in the current data package: None 4. Are there multiple versions of the dataset? No METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: The pollen and ovule number were counted in the lab with a microscope. The flower number and flower longevity per flower were recorded from several plants in a natural population. The flower length were measured by digital calipers in each inflorescence. The dry biomass per flower was weighed to the nearest 0.01 mg after dried at 80°C (24 h). In addition, all fruits per inflorescence were collected at the end of the fruiting season (~30 days after flower wilting), the seed production, fruit set, and bud developmental success per position or blooming stage were counted at lab 2. Methods for processing the data: Because data on floral traits did not conform to normal distribution by the way of Shapiro-Wilk normality test, we tested the effects of floral position, flowering sequence and their interaction on floral traits intra-inflorescence by Generalized linear mixed models (GLMM). To detect the effect of floral positions, flowering sequences and their interaction on floral traits within inflorescence, we treated floral position, flowering sequence as continuous and fixed factors, and “plant” as a random factor; floral dry biomass, flower length were conducted in a normal distribution (identity-link), and the flower number, pollen production and flower longevity were conducted in Poisson distribution (log-link). The flowering sequence of S. przewalskii does not follow a linear positional pattern within the inflorescence, and it is a decoupled blooming inflorescence, so the possible combinations could be taken into consideration when we performed a generalized linear mixed model (GLMM). The Shapiro-Wilk normality test results indicated that the data on reproductive success did not follow the normal distribution, and the size of the inflorescence could interfere with reproductive success. Therefore, we tested the effects of buds thinning, floral position, and pollination type and their interactions on seed production, fruit set and bud developmental success in architectural effects design by the GLMM model, and floral nodes number as random factors for architectural effects. In addition, we used generalized linear model (GLM) to detect the effects of buds thinning, flowering stage, pollination type and their interactions on seed production, fruit set and bud developmental success in resource competition design, with the flower number as a fixed covariate. During the process of analysing in GLM or GLMM model, binomial distribution (identity-link) was used on fruit set and bud developmental success, and the Poisson distribution (log-link) was applied on seed production. For each GLM and GLMM model, we checked the overdispersion by the equation of “deviance (fit.reduced)/df.residual (fit.reduced)”, and adopted the “quasipoisson” or “quasibinomial” distribution family to replace “poisson” or “binomial”. Reproductive success paired comparisons among different floral positions or flowering stages within inflorescences were performed by calculating the estimated marginal means using the “emmeans” package in R (Lenth, 2018), and a similar way was used to compare the same floral position or flowering stage between different inflorescences treatment. 3. Instrument- or software-specific information needed to interpret the data: All statistical analyses of this data were conducted in R version 4.1.1, for the Generalized linear mixed models (GLMM) and by the package lme4 (Bates et al., 2015), and the result of GLMM model is switched to analysis of variance table by the car package, when the categorical variable as a factor. The paired compared with estimated marginal means by the emmeans package (Lenth, 2018). 4. Standards and calibration information, if appropriate: None 5. Environmental/experimental conditions: Field experimental conditions 6. Describe any quality-assurance procedures performed on the data: All the data can be performed successfully 7. People involved with sample collection, processing, analysis and/or submission: Hao Wang, Zhi-Qiang Zhang, Bo Zhang, Li-Ping Wang, Wen Guo, Ye Fang, Qing-Jun Li DATA-SPECIFIC INFORMATION FOR: [FILENAME] 1. Number of variables: The data of Wang_2022_dataset of manusicript contained 11 variables. 2. Number of cases/rows: The data of Wang_2022_dataset of manusicript contained 8 cases. 3. Variable List: The data of Wang_2022_dataset of manusicript contained 11 sheets. including sex allocation, flower length, dry biomass, flower longevity, flower number, plant reproductive success (a), plant reproductive success (b), flower nodes number (a), flower number (b), pollinator foraging behavior, xylem and phloem size. In the sheets of plant reproductive success (a), plant reproductive success (b), flower nodes number (a), flower number (b), I indicated intact inflorescence, T indicated bud thinning inflorescence, O indicated open-pollination inflorescence, H indicated hand-pollination inflorescence, E indicated early stage blooming flower, L indicated early later blooming flower. B indicated basal position, M indicated middle position, D indicated distal position. 4. Missing data codes: n None 5. Specialized formats or other abbreviations used: None