Peony is a famous ornamental and medicinal plant in China, and peony hybrid breeding is an important means of germplasm innovation. However, research on the genome of this species is limited, thereby hindering the genetic and breeding research on peony. In the present study, simple sequence repeat (SSR) locus analysis was performed on expressed sequence tags obtained by the transcriptome sequencing of Paeonia using Microsatellite software. Primers with polymorphism were obtained via polymerase chain reaction amplification and electrophoresis. As a result, a total of 86,195 unigenes were obtained by assembling the transcriptome data of Paeonia. Functional annotations were obtained in seven functional databases including 49,172 (Non-Redundant Protein Sequence Database: 57.998 SSR loci were distributed in 17,567 unigenes containing SSR sequences, and the SSR distribution frequency was 25.52%, with an average of one SSR sequence per 4.66 kb. Mononucleotide, dinucleotide, and trinucleotide were the main repeat types, accounting for 55.74%, 25.58%, and 13.21% of the total repeat times, respectively. Forty-five pairs of the 100 pairs of primers selected randomly could amplify clear polymorphic bands. The polymorphic primers of these 45 pairs were used to cluster and analyze 16 species of peony. The new SSR molecular markers can be useful for the study of genetic diversity and marker-assisted breeding of peony.

No abstract

Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient method of predicting the AGB of a shrub community is essential for studying the spatial patterns and ecological functions of the desert region. Even though there are several entries in the literature on the AGB prediction of desert shrub communities using remote sensing data, the applicability and accuracy of airborne LiDAR data and prediction methods have not been well studied. We first extracted the elevation, density and intensity variables based on the airborne LiDAR, and then sample plot-level AGB prediction models were constructed using the parametric regression (nonlinear regression) and nonparametric methods (Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting Machine, and Multivariate adaptive regression splines). We evaluated accuracies of all the AGB prediction models we developed based on the fit statistics. Results showed that: (1) the elevation, density and intensity variables obtained from LiDAR point cloud data effectively predicted the AGB of the desert shrub community at a sample plot level, (2) the kappa coefficient of nonlinear mixed-effects (NLME) model obtained was 0.6977 with an improvement by 13% due to the random effects included into the model, and (3) the nonparametric model, such as Support Vector Machine showed the best fit statistics (R2 = 0.8992), which is 28% higher than the NLME-model, and effectively reduced the heteroscedasticity. The AGB prediction model presented in this paper, which is based on the airborne LiDAR data and machine learning algorithm, will provide a valuable tool to the managers and researchers for evaluating desert ecosystem productivity and monitoring desertification.