樱桃属(Cerasus)植物根围微生物多样性

研究了樱桃属(Cerasus) 5种植物本溪山樱(Cs)、大青叶(Cp)、马哈利(Cm)、考特(Cap)、早红宝石(Ca)各主要生长期根围微生物数量、种群组成、Shannom-Wiener指数(H)、丰富度(S)、Pielou均匀度指数(J)、Simpson 优势度(D)和优势微生物种群变化动态.从樱桃根围共分离到细菌18属,优势菌属主要为假单胞菌属(Pseudomonas)、芽孢杆菌属(Bacillus)和黄杆菌属(Flavobacterium);放线菌为链霉菌的12个类群,优势类群主要为白色类群(Albosporus)、吸水类群(Hygroscopicus)和黄色类群(Flavus);真菌6属,优势菌属为青霉属(Penicillium).樱桃根围微生物的多样性指数(H)、丰富度指数(S)、均匀度指数(J) 及优势度指数(D) 随樱桃种类不同而发生变化,细菌的多样性和丰富度指数为Ca＞Cs＞Cp＞Cm＞Cap,放线菌为Ca＞Cp＞Cs＞Cm＞Cap,真菌为Cm＞Ca＞Cp＞Cs＞Cap.各生长期根围微生物种类与数量发生变化,新梢迅速生长期Ca、Cs、Cp和Cm根围微生物种类与数量较多,新梢停长期Cap根围微生物种类与数量较多,落叶期5种樱桃属植物根围微生物种类与数量均较少.     

Estimating the first flowering and full blossom dates of Yoshino cherry (Cerasus × yedoensis ‘Somei-yoshino’) in Japan using machine learning algorithms

Climate change alters the phenology of various plants. For example, increasing temperatures shift the first flowering and full blossom days of Yoshino cherry trees and affect cultural events related to cherry blossoms. We developed models to estimate the first flowering and full blossom dates of Yoshino cherry in Japan based on temperature and phenological data observed at 82 stations in Japan for 68 years (1953–2020). Three machine learning algorithms, namely, the random forest (RF), artificial neural network (ANN), and gradient boosting decision tree (GBDT) algorithms, were utilized, and the hyperparameters were optimized using Optuna. The GBDT models produced the best estimation accuracy, with an overall root mean square error (RMSE) = 1.53 and 1.48 days for the first flowering date and full blossom date, respectively. Furthermore, our analysis using Shapley Additive Explanations (SHAP) revealed that in the RF and GBDT models, the low temperature in winter and high temperature in spring would advance the estimated first flowering and full blossom dates.