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A new angiosperms molecular specimen treatment method for field use
Feng-Jiao SHEN, Qian-Qian REN, Qi DONG, Li ZHU, Jian-Fang ZHANG, Jing YANG, Ran ZHANG, Hong-Zhu LIANG, Jian-Cheng ZHAO, Shuo SHI
Chin J Plan Ecolo    2017, 41 (7): 787-794.   doi: 10.17521/cjpe.2016.0322
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Aims This study aims to identify a more convenient drying method for obtaining molecular specimen of angiosperms in the field than the conventional silica gel drying method. Methods The leaves of Prunus serrulata var. lannesiana and Liriope spicata were dried under temperatures of 150 °C, 80 °C, 40 °C as well as under natural conditions, and by the silica gel drying method, respectively. The DNA extracts of various specimens were then analyzed using techniques of spectrophotometer detection, electrophoresis and PCR to evaluate the impacts of different drying treatments to the genomic DNA of testing plants. Important findings The concentrations of total DNA were higher for the specimens dried at 40 °C treatment and by the silica gel drying method than other treatments when assessed by the techniques of spectrophotometer detection and electrophoresis. The concentration of PCR products was highest in the specimens dried at 40 °C. Based on the results, the 40 °C drying can be recommended for obtaining molecular specimens of angiosperms because of its minimum degree of degradation, for convenience of operation and avoiding carrying large amounts of silica gel in field investigations.

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Point pattern analysis under conditions of replicated sampling
Xin-Ting WANG, Wei-Hua ZHANG, Chao JIANG, Cun-Zhu LIANG
Chin J Plan Ecolo    2017, 41 (5): 577-584.   doi: 10.17521/cjpe.2016.0383
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Aims The analysis of point patterns, which deals with data sets consisting of mapped locations of organisms in a study region, is especially important to plant ecological studies because the locations of plants can often be approximated as points. However, few studies used point pattern analysis with data collected by replicated sampling a principle procedure of acquiring data in ecological research. Therefore, we explore the applicability of point pattern analysis under conditions of replicated sampling in this studyMethodsThree replicated 5 m × 5 m plots of homogenous communities were established on a site with eight years of restoration in Nei Mongol steppe. In each plot, the locations of individuals in Leymus chinensis and Stipa grandis populations were mapped. O-Ring function was used to describe the population patterns and species association between L. chinensis and S. grandis for each plot as well as the integrative data of the three replicates.Important findings Population patterns and species associations differed among the three replicated plots. This illustrates that if point pattern analysis was applied to describe the population patterns and species associations only by using data from a single plot sampling, the results could be misleading. Whereas it would be more reliable to integrate the data of replicated plots in the point pattern analysis because in this way the resulting O-Ring function is a weighted average, where the weight is the number of points in the replicate i divided by the total number of points in all replicated plots.

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Using approximate Bayesian computation to infer photosynthesis model parameters
Ji-Ye ZENG, Zheng-Hong TAN, Nobuko SAIGUSA
Chin J Plan Ecolo    2017, 41 (3): 378-385.   doi: 10.17521/cjpe.2016.0067
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We developed a method, namely Adaptive Population Monte Carlo Approximate Bayesian Computation (APMC), to estimate the parameters of Farquhar photosynthesis model. Treating the canopy as a big leaf, we applied this method to derive the parameters at canopy scale. Validations against observational data showed that parameters estimated based on the APMC optimization are un-biased for predicting the photosynthesis rate. We conclude that APMC has greater advantages in estimating the model parameters than those of the conventional nonlinear regression models.

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A new technology of modulated Chl a fluorescence image: In vivo measurement of the PSII maximum photochemical efficiency and its heterogeneity within leaves
Da-Yong FAN, Zeng-Juan FU, Zong-Qiang XIE, Rong-Gui LI, Shu-Min ZHANG
Chin J Plan Ecolo    2016, 40 (9): 942-951.   doi: 10.17521/cjpe.2016.0001
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The spatial photosynthetic heterogeneity within leaves is an important prerequisite for the studies on the photosynthetic model, the mechanism(s) of photoinhibition and light protection, etc. However, currently the in vivo measurement of the spatial photosynthetic heterogeneity within leaves is difficult. The present study improved the device assembled by Vogelmann & Evans (2002), thereby acquired the photosystem II (PSII) maximum photochemical efficiency (Fv/Fm) images within leaves. Finally, these images were processed and data of Fv/Fm and its spatial variations could be obtained, with the aid of MATLAB software. Based on the innovative technique, an investigation of the effects of strong light on the Fv/Fm and its spatial heterogeneity within leaves has been carried out. It was found that Fv/Fm within leaves was not homogonous. Strong light led to a general decrease of Fv/Fm (PSII photoinhibition) across leaf section, and the palisade tissue close to the epidermis layer had high tolerance to photoinhibition. Compared with control, short-term photoinhibition caused a larger spatial variation of Fv/Fm within leaves, which may be related to the chloroplast-avoidance response induced by high-fluence. On the contrary, long-term light inhibition led to a smaller spatial variation of Fv/Fm within leaves, indicating such mechanism is no longer effective. Compared to other types of chlorophyll fluoremeter, the device in the present study can in vivo obtain the panoramic picture of Fv/Fm within leaves, providing a powerful tool for the studies on the mechanism(s) attributed to the spatial heterogeneity of photosynthetic capacity of leaf, which is critical for the understanding on several hot spots in the research field of photosynthesis.

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Application of a rice simulation model in high temperature sensitivity study
SAYRAN·Waley, LI Bing-Bai, ZHANG Jia-Hua, and YANG Shen-Bin
Chin J Plan Ecolo    2014, 38 (5): 515-528.   doi: 10.3724/SP.J.1258.2014.00048
Abstract358)      PDF (690KB)(1165)       Save

Aims For adoption and localization of the high precision rice growth model ORYZA2000, the model parameters were calibrated and high-temperature sensitivity analysis was performed based on observed data for five rice varieties at nine experimental stations and daily meteorological data in Jiangsu Province.
Methods The latest version of ORYZA2000 (V2.13) was used in this study. The model parameters were calibrated using the observed data for three rice varieties at five experimental stations, and then the aboveground biomass, leaf area index, and final yield were estimated for two other rice varieties at four experimental stations for model validation; a t-test was performed for quality evaluation. By using the validated model and raising the temperature at different time periods, a simulation of high temperature impact on rice biomass and yield was carried out. The simulation results were compared with the observational data from the greenhouse experiments assessing the high temperature responses of the rice varieties studied.
Important findings The results show that after calibration, the model parameters reliably simulated the dynamics of biomass accumulation and leaf area index development in the rice varieties studied; the simulated values are consistent with the observed values. The total biomass, panicle biomass, and final yield decreased by 12%–25% compared to the control (CK) when the growth temperature was raised to 35 °C for 3, 5 and 7 consecutive days from booting to flowering stages. Those values decreased by 18%–31% when the temperature was raised to 38 °C and by 20%–38% when the temperature was raised to 41 °C over the same periods. In general, the magnitudes of decline in the growth of rice varieties from model simulations were comparable with controlled laboratory observations. ORYZA2000 model could be applied to predict rice response to temperature increase on the basis of crop parameter calibrations.

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