Forests suffer from heavy losses due to the occurrence of fires. A prediction model based on environmental condition, such as meteorological and vegetation indexes, is considered a promising tool to control forest fires. The construction of prediction models can be challenging due to (i) the requirement of selection of features most relevant to the prediction task, and (ii) heavily imbalanced data distribution where the number of large-scale forest fires is much less than that of small-scale ones. In this paper, we propose a forest fire prediction method that employs a sparse autoencoder-based deep neural network and a novel data balancing procedure. The method was tested on a forest fire dataset collected from the Montesinho Natural Park of Portugal. Compared to the best prediction results of other state-of-the-art methods, the proposed method could predict large-scale forest fires more accurately, and reduces the mean absolute error by 3–19.3 and root mean squared error by 0.95–19.3. The proposed method can better benefit the management of wildland fires in advance and the prevention of serious fire accidents. It is expected that the prediction performance could be further improved if additional information and more data are available.

Interspecific hybridization has long been recognized as a pivotal process in plant evolution and speciation. It occurs fairly common in the genera of the subtribe Pyrinae. In Eriobotrya, a small tree genus of Pyrinae, E. prinoides var. daduheensis has been recognized as either a variety of E. prinoides, a natural hybrid between E. prinoides and E. japonica, or a variety of E. japonica. However, to date, there has been no convincing evidence on its status. Four nuclear genes and two chloroplast regions were sequenced in 89 individuals of these three Eriobotrya taxa from two locations where they coexist. A few fixed nucleotide substitutions or gaps were found in each of the investigated nuclear and chloroplast loci between E. japonica and E. prinoides. Of the 35 individuals of E. prinoides var. daduheensis, 33 showed nucleotide additivity of E. japonica and E. prinoides in at least one nuclear gene, and 10 of them harboured nucleotide additivity at all the four nuclear genes. Most haplotypes of E. prinoides var. daduheensis were also shared with those of E. japonica and E. prinoides. In the two chloroplast regions, 28 and 7 individuals were identical with E. japonica and E. prinoides, respectively. Our study provides compelling evidence for a hybrid status for E. prinoides var. daduheensis. Most hybrid individuals are later-generation hybrids. Both E. japonica and E. prinoides can serve as female parent. Differential adaptation might maintain the species boundary of E. prinoides and E. japonica in the face of hybridization and potential introgression.

Aluminum (Al) affects the yield of forest trees in acidic soils. The oil tea plant (Camellia drupifera Lour.) has high Al tolerance, with abundant phenolic compounds in its leaves, especially flavonoid compounds. The role of these flavonoids in the Al resistance of oil tea plants is unclear. In this metabolomic study of C. drupifera under Al stress, ultra-pressure liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) was utilized to identify metabolites, while principal component analysis, cluster analysis, and orthogonal partial least squares discriminant analysis were applied to analyze the data on the flavonoid metabolites. The leaf morphology of C. drupifera revealed significant damage by excess aluminum ions under each treatment compared with the control group. Under Al stress at 2 mmol/L (GZ2) and 4 mmol/L (GZ4), the total flavonoid content in C. drupifera leaves reached 24.37 and 35.64 mg/g, respectively, which are significantly higher than the levels measured in the control group (CK) (p < 0.01). In addition, we identified 25 upregulated and 5 downregulated metabolites in the GZ2 vs. CK comparison and 31 upregulated and 7 downregulated flavonoid metabolites in GZ4 vs. CK. The results demonstrate that different levels of Al stress had a significant influence on the metabolite profile of C. drupifera. It was found that the abundance of the 24 differential flavonoid metabolites was gradually elevated with increasing concentrations of Al stress, including catechin, epicatechin, naringenin-7-glucoside, astilbin, taxifolin, miquelianin, quercitrin, and quercimeritrin. Moreover, the most significant increase in antioxidant activity (about 30%) was observed in C. drupifera precultured in leaf extracts containing 7.5 and 15 μg/mL of active flavonoids. The qRT-PCR results showed that the expression levels of key genes involved in the synthesis of flavonoids were consistent with the accumulation trends of flavonoids under different concentrations of Al. Therefore, our results demonstrate the key role of flavonoid compounds in the oil tea plant C. drupifera in response to Al stress, which suggests that flavonoid metabolites in C. drupifera, as well as other aluminum-tolerant plants, may help with detoxifying aluminum.

Commelina danxiaensis (Commelinaceae), a remarkable new species from Mount Danxia, Guangdong Province, China, is described and illustrated. This species is similar to C. communis in inflorescences and flowers but readily distinguishable in its nearly erect stems, larger flowers, and different petal colouration.