Chemical characteristics of Falcataria moluccana wood infested by Boktor stem borer (Xystrocera festiva)

Haneda NF, Supriatna AH, Shabrina H, Istikorini Y, Siregar UJ, Wahyudi I. 2021. Chemical characteristics of Falcataria moluccana wood infested by Boktor stem borer (Xystrocera festiva). Biodiversitas 22: 4203-4208. Boktor (Xystrocera festiva Thoms.) is the most destructive and detrimental pest in Falcata plantations in Indonesia. It attacks the inner bark of sapwood when the tree reaches 3-years-old. This study aimed to analyze the chemical characteristics of Falcata woods, which were infested with Boktor in trees aged 3 and 4 years, compared to the healthy tree at 2 years old. The primary chemical components and type of extractive compounds were analyzed using standard procedures. The results showed a decrease in hemicellulose and an increase in lignin content of infected wood. This condition occurs because Boktor larvae prefer hemicellulose rather than lignin, which is related to the presence of enzymes in the digestive system of the larvae. The decreasing extractive contents as trees grow and undergo age-related shifting metabolism can be alleged as the start of Boktor infestation in 3-years-old trees. Boktor infestation also altering the wood chemical components by degrading the lignin that produced 4-vinyl-syringol. The isoeugenol compound is thought to prevent Boktor infestation in the early period of tree growth.

Genome-wide identification of lncRNAs, miRNAs, mRNAs, and their regulatory networks involved in tapping panel dryness in rubber tree (Hevea brasiliensis)

Noncoding RNAs (ncRNAs) play pivotal roles in various biological processes in plants. However, the role of ncRNAs in tapping panel dryness (TPD) of rubber tree (Hevea brasiliensis) is largely unknown. Here, the whole transcriptomes of bark tissues from healthy and TPD trees were performed to identify differentially expressed long ncRNAs (DELs), microRNAs/miRNA (DEMs), genes (DEGs), and their regulatory networks involved in TPD. A total of 263 DELs, 174 DEMs, and 1,574 DEGs were identified in the bark of TPD tree compared with that of healthy tree. KEGG analysis revealed that most of the DEGs and targets of DELs and DEMs were mainly enriched in metabolic pathways, biosynthesis of secondary metabolites, and plant hormone signal transduction. Additionally, the majority of DEGs and DELs related to rubber biosynthesis were down-regulated in TPD trees. Furthermore, 98 DEGs and 44 DELs were targeted by 54 DEMs, 190 DEGs were identified as putative targets of 56 DELs, and two and 44 DELs were predicted as precursors and endogenous target mimics (eTMs) of two and six DEMs, respectively. Based on these, the DEL-DEM-DEG regulatory network involved in TPD was constructed, and 13 hub DELs, three hub DEMs and two hub DEGs were identified. The results provide novel insights into the regulatory roles of ncRNAs underlying TPD and lay a foundation for future functional characterization of lncRNAs, miRNAs, and genes involved in TPD in rubber tree.

A Comparative Study on the Microbial Communities of Rhynchophorus ferrugineus (Red Palm Weevil)-Infected and Healthy Palm Trees

Several studies have investigated palm trees’ microbiota infected with red palm weevil (RPW) (Rhynchophorus ferrugineus), the major pest of palm trees. This study compared the microbial communities of infected and uninfected palm trees in the Hail region, Northern Saudi Arabia, determined by high-throughput 16S rRNA gene sequencing by Illumina MiSeq. The results indicated that taxonomic diversity variation was higher for infected tree trunk than the healthy tree trunk. Soil samples from the vicinity of healthy and infected trees did not have a significant variation in bacterial diversity. Myxococcota, Acidobacteriota, and Firmicutes were the dominant phyla in RPW-infected tree trunk, and Pseudomonadaceae was the most prominent family. This study is the first report on the characterization of RPW-infected and healthy palm trees’ microbiome.

Impacts of European spruce bark beetle infestations on Norway spruce BVOC emissions

<p>Tree killing by spruce bark beetles (<em>Ips typographus</em>) is one of the main disturbances to Norway spruce (<em>Picea abies</em>) forests in Europe and the risk of outbreaks is amplified by climate change with effects such as increased risk of storm felling, tree drought stress and an additional generation of spruce bark beetles per year<sup>[1]</sup>. The warm and dry summer of 2018 triggered large outbreaks in Sweden, the increased outbreaks are still ongoing and affected about 8 million m<sup>3</sup> forest in 2020<sup>[2]</sup>. This is the so far highest record of trees killed by the spruce bark beetle in a single year in Sweden<sup>[2]</sup>. In 1990-2010, the spruce bark beetle killed on average 150 000 m<sup>3</sup> forest per year in southern Sweden<sup>[3]</sup>. Bark beetles normally seek and attack Norway spruces with lowered defense, i.e. trees that are wind-felled or experience prolonged drought stress<sup>[4]</sup>. However, as the number of bark beetle outbreaks increase, the risk of attacks on healthy trees also increase<sup>[5]</sup>. This causes a higher threat to forest industry, and lowers the possibilities to mitigate climate change in terms of potential decreases in carbon uptake if the forests die<sup>[4,5]</sup>. Norway spruce trees normally defend themselves by drenching the beetles in resin<sup>[6]</sup>. The resin in turn contains different biogenic volatile organic compounds (BVOCs), which can vary if the spruce is attacked by bark beetles or not<sup> [4,6]</sup>. The most abundant group of terpenoids (isoprene, monoterpenes and sesquiterpenes), is most commonly emitted from conifers, such as Norway spruce<sup>[7,8]</sup>. The aim of this study was to enable a better understanding of the direct defense mechanisms of spruce trees by quantifying BVOC emissions and its composition from individual trees under attack</p><p>To analyze the bark beetles’ impact on Norway spruce trees a method was developed using tree trunk chambers and adsorbent tubes. This enables direct measurements of the production of BVOCs from individual trees. Three different sites in Sweden, with different environmental conditions were used for the study and samples were collected throughout the growing season of 2019. After sampling, the tubes were analyzed in a lab using automated thermal desorption coupled to a gas chromatograph and a mass spectrometer to identify BVOC species and their quantity.</p><p>The preliminary results show a strong increase in BVOC emissions from a healthy tree that became infested during the data collection. The finalized results expect to enable better understanding of how spruce trees are affected by insect stress from bark beetles, and if bark beetle infestation will potentially result in increased carbon emission in the form of BVOCs.</p><p><strong>References</strong></p><p>[1] Jönsson et al. (2012). Agricultural and Forest Meteorology 166: 188–200<br>[2] Skogsstyrelsen, (2020). https://via.tt.se/pressmeddelande/miljontals-granar-dodades-av-granbarkborren-2020?publisherId=415163&releaseId=3288473<br>[3] Marini et al. (2017). Ecography, 40(12), 1426–1435.<br>[4] Raffa (1991). Photochemical induction by herbivores. pp. 245-276<strong><br></strong>[5] Seidl, et al. (2014). Nature Climate Change, 4(9), 806-810. <br>[6] Ghimire, et al. (2016). Atmospheric Environment, 126, 145-152.<br>[7] Niinemets, U. and Monson, R. (2013). ISBN 978-94-007-6606-8<br>[8] Kesselmeier, J. and Staudt, M. (1999). Journal of Atmospheric Chemistry, 33(1), pp.23-88</p>

Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing

Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time- and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees.

Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning

Combining modern technology and agriculture is an important consideration for the effective management of oil palm trees. In this study, an alternative method for oil palm tree management is proposed by applying high-resolution imagery, combined with Faster-RCNN, for automatic detection and health classification of oil palm trees. This study used a total of 4172 bounding boxes of healthy and unhealthy palm trees, constructed from 2000 pixel × 2000 pixel images. Of the total dataset, 90% was used for training and 10% was prepared for testing using Resnet-50 and VGG-16. Three techniques were used to assess the models’ performance: model training evaluation, evaluation using visual interpretation, and ground sampling inspections. The study identified three characteristics needed for detection and health classification: crown size, color, and density. The optimal altitude to capture images for detection and classification was determined to be 100 m, although the model showed satisfactory performance up to 140 m. For oil palm tree detection, healthy tree identification, and unhealthy tree identification, Resnet-50 obtained F1-scores of 95.09%, 92.07%, and 86.96%, respectively, with respect to visual interpretation ground truth and 97.67%, 95.30%, and 57.14%, respectively, with respect to ground sampling inspection ground truth. Resnet-50 yielded better F1-scores than VGG-16 in both evaluations. Therefore, the proposed method is well suited for the effective management of crops.

Study of the current vegetation of the historical lava flows of the Arafo Volcano, Tenerife, Canary Islands, Spain

Vegetation research on the lava flows of the historic volcanic eruption of 1705 in Arafo, Tenerife, Canary Islands, is presented. The study area located in the 830000-year-old valley of Güímar was created after a massive landslide 47 km3 in volume. The research is divided into three parts, which cover an altitudinal range from around 35 to 1583 m a.s.l. from the Lower-semiarid Inframediterranean up to the Lower-dry lower-Mesomediterranean bioclimatic belts. First, a phytosociological study of the vegetation present in the area was made and concluded that richness in pioneer communities form a vegetation complex with a high degree of endemicity. Two new associations and four pioneer communities are proposed. Especially notable are the communities of Stereocauletum vesuviani and the pioneer communities of Pinus canariensis. The second part of the research was a field sampling study of 450 individuals of Pinus canariensis, which were measured at different altitudes to obtain data about the colonization dynamics of this species on this 300 years old substrate. We found that stem diameter seems to be a good indicator for healthy tree development at a range between 700 to 1300 m asl, which corresponds to the pine forest as potential vegetation and that many individuals show signs of nutrient deficiency. The third part consists of the publication of two new populations of the Canarian endemism Himantoglossum metlesicsianum, a highly endangered orchid. The monitoring of these two populations has recently begun, and further research will be conducted on all three aspects of this publication, which will be presented and expanded upon in the future.

Effect of Different Chemical Formulation on Agarwood (Aquilaria malaccensis) Resin Weight

Agarwood is described as fragrance, smelling wood that is usually derived from the trunk of genus Aquilaria The objective of this study was to develop and evaluate four chemical-based inoculants to induce karas tree to form agarwood resin. Four chemical inoculants were applied into 6 years old karas tree (Aquilaria malaccensis) plantations in Slim River, Perak, Malaysia using dripping techniques. The inoculant used were labelled as A, B, C and D. Commercial inoculant was also used for comparison purposes and labelled as K while a healthy tree, act as control was labelled as N. After four months of inoculation, these samples were cut down and discoloured tissue samples were collected. Solvent extraction method using dichloromethane (DCM) was carried out to obtain the resin crude extract from plant tissues of the samples. All chemical formulation used were succesfully stimulated sample trees to produce resin. Inoculant B produced the highest crude extract recovery (5.49%) compared to inoculant C (4.78%) followed by A (4.32%), D (2.98%), K (1.07%) and N (0.13%). From this study, inoculant B showed the highest yield of resin. Hence, result from current study has the potential to contribute in enhancing new types of inoculant production for the agarwood industries in Malaysia.