Probabilistic seasonal rainfall forecasting is of great importance for stakeholders such as farmers and policymakers to assist in developing risk management strategies and to inform decisions. In practice, there are two kinds of commonly used tools, dynamical models and statistical models, to provide probabilistic seasonal rainfall forecasts. Dynamical models are based on physical processes but are usually expensive to operate and implement, and rely overly on initial conditions. Statistical models are easy to implement but are usually based on simple or linear relationships between observed variables. Recently, machine learning techniques have been widely used in climate projection and perform well in reproducing historical climate. For these reasons, we conducted a case study in Australia by developing a machine learning-based probabilistic seasonal rainfall forecasting model using multiple large-scale climate indices from the Pacific, Indian and Southern Oceans. Rainfall probabilities of exceeding the climatological median for upcoming seasons from 2011 to 2018 were successively forecasted using multiple climate indices of precedent six months. The performance of the model was evaluated by comparing it with an officially used forecasting model, the SOI (Southern Oscillation Index) phase model (SP) operated by Queensland government in Australia. Results indicated that the random forest (RF) model outperformed the SP model in terms of both distinct forecasts and forecasting accuracy. The RF model increased the percentages of distinct forecasts to 64.9% for spring, to 71.5% for summer, to 65.8% for autumn, and to 63.9% for winter, 1.4 ∼ 3.2 times of the values from the SP model. Forecasting accuracy was also greatly increased by 28%, 167%, 219%, and 76% for four seasons respectively, compared to the SP model. The proposed rainfall forecasting model is based on readily available data, and we believe it can be easily extended to other regions to provide seasonal rainfall outlooks.

Two new Diptacus species and one new Trimeroptes species (Acari: Eriophyoidea: Diptilomiopidae) are described and illustrated from China: Diptacus suichangensis sp. nov. on Aralia chinensis L. (Araliaceae) and Diptacus pyracanthae sp. nov. on Pyracantha fortuneana (Maxim.) Li (Rosaceae), and Trimeroptes longlinensis sp. nov. on Carpinus sp. (Betulaceae). These new species were found to be vagrant on the lower surface of their associated plants leaves, albeit with no apparent damage observed.

In this paper, three new species of eriophyoid mites belonging to the subfamily Nothopodinae from Guangxi are described and illustrated. They are Nothopoda maesae sp. nov. collected on Maesa perlaria (Lour.) Merr. (Myrsinaceae), Nothopoda camelliae sp. nov. collected on Camellia oleifera Abel (Theaceae), and Nothopoda nepalense sp. nov. collected on Alnus nepalensis D. Don (Betulaceae). All the species are vagrants on the lower leaf surface, causing no apparent damage to their host plants. http://www.zoobank.org/urn:lsid:zoobank.org:pub:4AC5CCA4-22FD-4B9B-B76D-35CDA24392C4

Chlorophytum comosum is a perennial ornamental plant in the family Liliaceae, it is also a valuable medicinal plant. To enrich the genetic resources of C. comosum, its chloroplast genome was determined by Illumina sequencing data. The chloroplast genome is a typical quadripartite structure with a size of 153,983 bp, of which the LSC region is 83,471 bp, the SSC region is 18,010 bp, and the pair of IR regions is 26,251 bp. The overall GC content is 37%. It contains 131 genes, including 85 protein-coding genes, 38 tRNA genes, and 8 rRNA genes. Phylogenetic analyses showed that C. comosum is closely related to Chlorophytum rhizopendulum. However, it can be distinguished from other plants. This study enriches the sequence resources of C. comosum and provides important data for the development of molecular identification markers.

This study aimed to characterize the sources and spatio-temporal distribution patterns of cytokinins in Dryocosmus kuriphilus and its induced galls, providing observational data on possible gall-inducing mechanisms. Specifically, the contents of six cytokinins (TZR, DZR, cis-Zeatin, trans-Zeatin, IP, and IPR) in the epidermal layer (IPR: 3.92 ± 0.15 ng/g), outer protective layer, inner protective layer, and nutritive layer of galls, as well as in D. kuriphilus at different developmental stages, were quantified by UPLC-MS/MS. The results showed that IPR (63.12 ± 2.55 ng/g) was relatively high in larvae. This observational finding is consistent with the hypothesis that cytokinins in D. kuriphilus may originate from the insect itself or from plant sources. Elevated cytokinin levels were found in plant galls compared to normal tissues.

Two new eriophyid mite species (Acari: Prostigmata: Eriophyoidea) were described from the host plants of the genus Callerya Endl. during a field survey in China: Tetra nitidae sp. nov. vagrant on leaves of Callerya nitida (Bentham) R. Geesink (Leguminosae), and Calepitrimerus calleryus sp. nov. vagrant on leaves of Callerya tsui (F. P. Metcalf) Z. Wei & Pedley. Both species caused no apparent symptoms in their host plants. Additionally, this paper provides a supplementary morphological description and illustrations of Fujianacarus wisterianis Song, Xue & Hong, 2008 (Eriophyoidea).