Search

Trait evolution analyses enable the comparison of characters amongst different species—a useful technique when inferring ancestral phenotypes based on a phylogeny of living taxa. The evolution of discrete characters can be mapped on the branches of a phylogenetic tree using stochastic mapping. Here we present sMap, a new program to perform stochastic mapping analyses.Key features characterising sMap are: a wide variety of models and prior distributions; the ability to use a posterior distribution of trees and to compute marginal likelihoods to perform model selection analyses; and the implementation of three kinds of characters: 'independent' characters, which do not interact with each other; 'dependent' characters, which co‐evolve at the same time; and 'conditioned' characters, whose state is determined by the state of other characters.Here we present two examples that show how sMap can be used to perform stochastic mapping analyses, produce robust results and answer new kinds of questions. sMap is freely available and distributed under a GPL licence, in a command‐line and Graphical User Interface version; a detailed user manual with examples and tutorials is also provided.The wide variety of algorithms implemented in sMap enables accurate analyses of the evolution of discrete characters. Conditioned characters can be used to study the interaction of simple traits to produce complex phenotypes. As a multiplatform and open‐source project, sMap can be used on a variety of systems and situations, such as academic research and teaching. [ABSTRACT FROM AUTHOR]

Summary: Phytochrome‐interacting factors (PIFs) play major roles in regulating plant growth and development, but their roles in drought stress remain elusive. Here, we cloned and characterized a maize (Zea mays) PIF transcription factor, ZmPIF1. The expression level of ZmPIF1 was significantly induced by independent drought and abscisic acid (ABA) treatments. The ZmPIF1 transgenic rice and Arabidopsis displayed water saving and drought resistance, which were associated with reduced a stomatal aperture and transpiration rate. Moreover, the ZmPIF1 transgenic rice were hypersensitive to exogenous ABA, while the endogenous ABA level was not significantly changed, suggesting that ZmPIF1 was a positive regulator of the ABA signalling pathway. Digital gene expression (DGE) results further indicated that ZmPIF1 participated in ABA signalling pathway and regulated the stomatal aperture in rice. In addition, grain yield and agronomic traits analysis over 4 years showed that ZmPIF1 was able to increase the grain yield through an increase in tiller and panicle numbers in transgenic rice. Overall, ZmPIF1 plays an important role in the ABA‐mediated regulation of stomatal closure to control water loss. ZmPIF1 can enhance water saving and drought resistance and improve the crop yield in rice, illustrating the capacity of ZmPIF1 for crop improvement. [ABSTRACT FROM AUTHOR]

Phytoliths, as one of the important sources of microfossils, have been widely used in paleobotany-related studies, especially in the grass family (Poaceae) where abundant phytoliths are found. Despite great efforts, several challenges remain when phytoliths are used in various studies, including the accurate description of phytolith morphology and the effective utilization of phytolith traits in taxon identification or discrimination. In this study, we analyzed over 1000 phytolith samples from 18 taxa representing seven main genera in the tribe Oryzeae (subfamily Ehrhartoideae) and five taxa in the subfamilies Bambusoideae and Pooideae. By focusing on Oryzeae, which has been extensively investigated in terms of taxonomy and phylogeny, we were able to evaluate the discrimination power of phytoliths at lower taxonomic levels in grasses. With the help of morphometric analysis and by introducing several machine learning algorithms, we found that 87.7% of the phytolith samples could be classified correctly at the genus level. In spite of slightly different performances, all four machine learning algorithms significantly increased the resolving power of phytolith evidence in taxon identification and discrimination compared with the traditional phytolith analysis. Therefore, we propose a pipeline of phytolith analyses based on machine learning algorithms, including data collection, morphometric analysis, model building, and taxon discrimination. The methodology and pipeline presented here should be applied to various studies across different groups of plants. This study provides new insights into the utilization of phytoliths in evolutionary and ecology studies involving grasses and plants in general. [ABSTRACT FROM AUTHOR]

The Chinese traditional chrysanthemum cultivar is a marvelous group of Chrysanthemum ×grandiflorum Tzvelv., whose floral characters are rich in variation. However, neither the values of floral characters for the classification of chrysanthemums nor the relevance of different characters are yet known. Moreover, the existing classification systems of chrysanthemums are incomplete and unsystematic. In the present study, we selected 16 floral morphological characters on which to carry out diversity and relativity analyses by seven multivariate statistical analysis methods, and we further studied the cultivar classification. The analyses of nested variance, diversity index, and principle components showed that 10 floral morphological characters were of great value for chrysanthemum classification. Clustering analysis classified these 10 characters into three types, in which the variation of the ray floret factor was the highest. The analysis of partial correlations showed that the petal type was significantly affected by the length of the ray florets, the bending state of the outer-layer ray florets, and the tip shape of the ray florets; besides these three characters, the flower head type was also affected by the angle of the outer-layer ray florets and the length and distribution of the disc florets. The multiple linear regression equations for petal type and flower head type were successfully established. All of the cultivars were classified into four groups and 18 forms by cluster and discriminant analyses. These results provide a reference that can be used for cultivar identification and classification of chrysanthemums. [ABSTRACT FROM AUTHOR]

Nachman and Heller recently suggested that previous estimates of therelative magnitude of fluctuating asymmetry in morphological characters have been biased because non-linear relationships between asymmetry and size have not been considered. This suggestion cannot generally be very important because (1) inclusion of a non-linear size term in regression models relating fitness components to asymmetry does notchange the general conclusion that asymmetry is an important predictor of fitness components, and (2) experimental studies uncoupling therelationship between asymmetry and size have generally found significant effects of asymmetry independent of body size on mating success and locomotion. [ABSTRACT FROM AUTHOR]