Journal of Northeast Forestry University

Two species of Acerophagus from Yunnan Province, China, are reported. One species, A. ovaliclavus sp. nov., is described as new to science, and one species, A. solidus Hayat is reported as new to China. A key to all described species from China based on females is given. All the type materials are deposited in the insect collections of Northeast Forestry University, China.

ANXA2(AnnexinA2), a calcium-dependent phospholipid binding protein, is involved in various Ca2+-related biological activities. In the present study, full-length cDNA of ANXA2 was isolated from the velvet antler tip tissue of sika deer (Cervus nippon hortulorum); the amino acid sequence and gene expression was analyzed by using bioinformatics and real-time reverse transcriptase polymerase chain reaction (RT-PCR) techniques. Nucleotide sequence analysis reveals that the full-length cDNA of the ANXA2 gene was 1372 bp, of which 1020 bp was in the open-reading frame (ORF) encoding 339 amino acids; its relative molecular weight was 38.3 kDa; and isoelectric point was 6.72. Sequence analysis indicates that the protein includes four conserved tandem-duplication ANX domains. The gene-accession nucleotide sequence number in GenBank is JX315571. Expression analysis by RT-PCR reveals that ANXA2 gene expression has a significant positive correlation with the antler-tissue mineralization process, indicating that this gene may play an important role in the regulation of antler-tissue mineralization.

This study addresses the increasing demand for large-diameter production timber, and considers the time and space variability of half-sib families of Pinus massoniana. Height, diameter at breast height (DBH) and timber volume of 440 open-pollinated half-sib progeny families were investigated in 14 progeny trials in different years and production regions. An evaluation of the genetic variation of all half-sib families was carried out during the sustainable rapid growth period and individual volumes were characterized as a major index. ANOVA analysis showed that there was considerable variance in the growth traits of most families in different years and on different sites. The variations caused by temporal and spatial changes of the mating system required three selection methods for analysis. The results show that there were differences among the heritabilities of different growth traits by different half-sib progenies. Average heritability values of height, DBH and volume were 0.33, 0.34 and 0.36, respectively. Forty-five superior families were selected in every progeny test, 12 were selected in progeny trials by different years and five in different habitat progeny trials. Three superior families (Gui GC553A, Gui GC414A and Gui GC431A) were selected, although in different years and production regions. The genetic gains of timber volume of these selected r families ranged from 1.20 to 47.00%, which could provide a foundation for superior wood property selection and serve as material for seed improvement and extension in surrounding areas.

Inventory recorded 4 genera and 11 species of mountain bamboos in Shennongjia, Central China. Of these, 4 species (Phyllostachys nigra var.henonis, Ph. heteroclada, Ph. nidularia andPh. bambusoides) belong to the monopodial bamboo, 4 species (Indocalamus latifolius, I. longiauritus, I. wilsoni andI. tessellatus) to the amphipodial bamboo, and 3 species (Fargesia spathacea, F. murielae andYushania confusa) to the sympodial bamboo. Monopodial bamboos grow on the gentle slopes below 1800 m asl, occurring from the subtropical belt to the temperate belt in the vertical climatic spectrum of the mountain; Sympodial bamboos occur in higher elevations, ranging from the warm temperate (1200 m), across the mediate temperate and cold temperate, to the mountaintops of the frigid temperate belt (3100 m). Amphipodial bamboos occur in all climate types except the frigid temperate belt. Three dominant species,F. spathacea, F. murielae andY. confusa, currently cover 12%, 9% and 3% of the mountain ranges in Shennongjia, respectively.

Voxel-based canopy profiling is commonly used to determine small-scale leaf area. Layer thickness and voxel size impact accuracy when using this method. Here, we determined the optimal combination of layer thickness and voxel size to estimate leaf area density accurately. Terrestrial LiDAR Stonex X300 was used to generate point cloud data for Masson pines (Pinus massoniana). The canopy layer was stratified into 0.10–1.00-m-thick layers, while voxel size was 0.01–0.10 m. The leaf area density of individual trees was estimated using leaf area indices for the upper, middle, and lower canopy and the overall canopy. The true leaf area index, obtained by layered harvesting, was used to verify the inversion results. Leaf area density was inverted by nine combinations of layer thickness and voxel size. The average relative accuracy and mean estimated accuracy of these combined inversion results exceeded 80%. When layer thickness was 1.00 m and voxel size 0.05 m, inversion was closest to the true value. The average relative accuracy was 92.58%, mean estimated accuracy 98.00%, and root mean square error 0.17. The combination of leaf area density and index was accurately retrieved. In conclusion, nondestructive voxel-based canopy profiling proved suitable for inverting the leaf area density of Masson pine in Hetian Town, Fujian Province.

Dead fine fuel moisture content (DFFMC) is a key factor affecting the spread of forest fires, which plays an important role in evaluation of forest fire risk. In order to achieve high-precision real-time measurement of DFFMC, this study established a long short-term memory (LSTM) network based on particle swarm optimization (PSO) algorithm as a measurement model. A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed. The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set. The surface fine dead fuel of Mongolian oak (Quercus mongolica Fisch. ex Ledeb.), white birch (Betula platyphylla Suk.), larch (Larix gmelinii (Rupr.) Kuzen.), and Manchurian walnut (Juglans mandshurica Maxim.) in the maoershan experimental forest farm of the Northeast Forestry University were investigated. We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy, meteorological, and spectral meteorological fusion methods. The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1% for Mongolian oak, 1.3% for white birch, 1.4% for larch, and 1.8% for Manchurian walnut, and these values were lower than those of the near-infrared method and the meteorological method. The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.

This paper introduced the Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs), which have been widely used in optimization of allocating. The combination way of the two optimizing algorithms was used in board allocating of furniture production. In the experiment, the rectangular flake board of 3650 mm×1850 mm was used as raw material to allocate 100 sets of Table Bucked. The utilizing rate of the pcard reached 94.14% and the calculating time was only 35 s. The experiment result proofed that the method by using the GA for optimizing the weights of the ANN can raise the utilizing rate of the board and can shorten the time of the desing. At the same time, this method can simultaneously searched in many directions, thus greatly increasing the probability of finding a global optimum.

Cork oak forests in Morocco are rich in resources and services thanks to their great biological diversity, playing an important ecological and socioeconomic role. Considerable degradation of the forests has been accentuated in recent years by significant human pressure and effects of climate change; hence, the health of the stands needs to be monitored. In this study, the Google Engine Earth platform was leveraged to extract the normalized difference vegetation index (NDVI) and soil-adjusted vegetation index, from Landsat 8 OLI/TIRS satellite images between 2015 and 2017 to assess the health of the Sibara Forest in Morocco. Our results highlight the importance of interannual variations in NDVI in forest monitoring; the variations had a significantly high relationship (p < 0.001) with dieback severity. NDVI was positively and negatively correlated with mean annual precipitation and mean annual temperature with respective coefficients of 0.49 and − 0.67, highlighting its ability to predict phenotypic changes in forest species. Monthly interannual variation in NDVI between 2016 and 2017 seemed to confirm field observations of cork oak dieback in 2018, with the largest decreases in NDVI (up to − 38%) in December in the most-affected plots. Analysis of the influence of ecological factors on dieback highlighted the role of substrate as a driver of dieback, with the most severely affected plots characterized by granite-granodiorite substrates.