Efficacy of synthetic lures for pine bark beetle monitoring

The Scots pine (Pinus sylvestris) plantations in central Europe are currently damaged by a large-scale infestation by bark beetles (Scolytinae). Ips acuminatus and Ips sexdentatus are among the most aggressive species causing infestations of pine trees that are currently simultaneously attacked by Ips typographus. In pine plantations prone to damage, it is therefore necessary to carry out the bark beetle monitoring. One of the used methods is the pheromone bark beetle trapping using synthetic lures. The efficacy of synthetic lures differs. We tested the efficacy of commercially available lures used in the protection of pine trees. In total, we deployed 10 trap series, each consisting of traps with eight different lures and two unbaited traps (controls). Ips acuminatus and I. sexdentatus were most abundantly captured in Pheagr-IAC- and Sexowit-baited traps. Interestingly, the spruce species I. typographus was also captured and most often found in traps with Pheagr-IAC and Erosowit Tube lures. The number of captured beetles was consistent with the gradation phase of bark beetles. Our results suggest the suitability of pheromone traps for bark beetle monitoring. The use of Sexowit can be recommended especially in southwestern Moravia, where I. sexdentatus occurs in high numbers in the long run. In other parts of the Czech Republic, Pheagr-IAC alone can be used with sufficient efficacy. The use of the Erosowit Tube lure is also suitable for I. typographus and I. sexdentatus monitoring.

Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees

The internal cycling of nitrogen (N) storage and consumption in trees is an important physiological mechanism associated with tree growth. Here, we examined the capability of near-infrared spectroscopy (NIR) to quantify the N concentration across tissue types (needle, trunk, branch, and root) without time and cost-consuming. The NIR spectral data of different tissues from slash pine trees were collected, and the N concentration in each tissue was determined using standard analytical method in laboratory. Partial least squares regression (PLSR) models were performed on a set of training data randomly selected. The full-length spectra and the significant multivariate correlation (sMC) variable selected spectra were used for model calibration. Branch, needle, and trunk PLSR models performed well for the N concentration using both full length and sMC selected NIR spectra. The generic model preformatted a reliable accuracy with R2C and R2CV of 0.62 and 0.66 using the full-length spectra, and 0.61 and 0.65 using sMC-selected spectra, respectively. Individual tissue models did not perform well when being used in other tissues. Five significantly important regions, i.e., 1480, 1650, 1744, 2170, and 2390 nm, were found highly related to the N content in plant tissues. This study evaluates a rapid and efficient method for the estimation of N content in different tissues that can help to serve as a tool for tree N storage and recompilation study.

Rainfall and Landslide Susceptibility in Hakha Environ in Northern Chin State, Myanmar

This research emphasizes the cause of landslides that occur in Hakha Town and its environ. The main aim is to investigate the distinct phenomena that result in a landslide and to provide suggestions that can reduce the risk of landslide in its prone area. Regarding the two phenomena, natural and man-made, the data on soil, steep slope, monsoon rainfall, pine forest areas, water sources, motor-car road area, population, and houses were collected by field survey, observation, and questionnaires. The collected data were processed and analyzed by using remote sensing methods, qualitative and quantitative methods, and Geographic Information System. According to the results, major causes of the landslides in the study area are found to be due to location lying between 1,830 meters (6,000 ft) and 2,440 meters (8,000 ft) above sea level and establishing of the settlements on steep slopes, receiving plenty of rainfall under the mountain climate with the extremely cold winter season, the existence of unstable and unconsolidated soil and lithology, extending construction of new roads and expansion of the existing roads, population growth and settling of more people in the urban area, and collapsing of big old pine trees. In conclusion, landslides in the study area are found resulting from combined activities of physical factors and human impacts.

Multi-Omics of Pine Wood Nematode Pathogenicity Associated With Culturable Associated Microbiota Through an Artificial Assembly Approach

Pinewood nematode (PWN), the causal agent of pine wilt disease (PWD), causes massive global losses of Pinus species each year. Bacteria and fungi existing in symbiosis with PWN are closely linked with the pathogenesis of PWD, but the relationship between PWN pathogenicity and the associated microbiota is still ambiguous. This study explored the relationship between microbes and the pathogenicity of PWN by establishing a PWN-associated microbe library, and used this library to generate five artificial PWN–microbe symbiont (APMS) assemblies with gnotobiotic PWNs. The fungal and bacterial communities of different APMSs (the microbiome) were explored by next-generation sequencing. Furthermore, different APMSs were used to inoculate the same Masson pine (Pinus massoniana) cultivar, and multi-omics (metabolome, phenomics, and transcriptome) data were obtained to represent the pathogenicity of different APMSs at 14 days post-inoculation (dpi). Significant positive correlations were observed between microbiome and transcriptome or metabolome data, but microbiome data were negatively correlated with the reactive oxygen species (ROS) level in the host. Five response genes, four fungal genera, four bacterial genera, and nineteen induced metabolites were positively correlated with the ROS level, while seven induced metabolites were negatively correlated. To further explore the function of PWN-associated microbes, single genera of functional microbes (Mb1–Mb8) were reloaded onto gnotobiotic PWNs and used to inoculate pine tree seedlings. Three of the genera (Cladophialophora, Ochroconis, and Flavobacterium) decreased the ROS level of the host pine trees, while only one genus (Penicillium) significantly increased the ROS level of the host pine tree seedlings. These results demonstrate a clear relationship between associated microbes and the pathogenicity of PWN, and expand the knowledge on the interaction between PWD-induced forest decline and the PWN-associated microbiome.

Distribution of organic carbon in Ultisol soils with citronella and pine vegetation, at Gayo Highlands, Aceh

Soil organic matter is an indicator of soil fertility. The purpose of this study was to analyse various forms of soil organic carbon in citronella plantation, citronella plantation under pine tree, and soil under pine tree. Soil organic carbon in various forms was analysed from soil samples taken from each horizon and soil profile. The soil profiles observed were ultisol profiles planted with citronella, citronella under pine tree, and under pine tree, and slopes; 0-8%, 8-15%, 15 -25%, and 25-40%, in order to obtain 12 soil profiles with a total of 39 soil samples. Ultisols planted with citronella had higher soil organic carbon than ultisols planted with citronella under pine tree and ultisols under pine trees. Based on the slope, the highest soil organic carbon was obtained in the soil with a slope of 0-8%, and decreased with increasing slope. Based on soil depth, the highest soil organic carbon was obtained in the upper horizon, compared to the horizon below. The highest total soil organic carbon was obtained at the soil surface horizon with a slope of 0-8% and citronella was planted. This pattern of total soil organic carbon is similar to that of sesquioxide bound organic carbon, but is not consistent with that of free clay bound organic carbon.

A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images

Pine wilt is a devastating disease that typically kills affected pine trees within a few months. In this paper, we confront the problem of detecting pine wilt disease. In the image samples that have been used for pine wilt disease detection, there is high ambiguity due to poor image resolution and the presence of “disease-like” objects. We therefore created a new dataset using large-sized orthophotographs collected from 32 cities, 167 regions, and 6121 pine wilt disease hotspots in South Korea. In our system, pine wilt disease was detected in two stages: n the first stage, the disease and hard negative samples were collected using a convolutional neural network. Because the diseased areas varied in size and color, and as the disease manifests differently from the early stage to the late stage, hard negative samples were further categorized into six different classes to simplify the complexity of the dataset. Then, in the second stage, we used an object detection model to localize the disease and “disease-like” hard negative samples. We used several image augmentation methods to boost system performance and avoid overfitting. The test process was divided into two phases: a patch-based test and a real-world test. During the patch-based test, we used the test-time augmentation method to obtain the average prediction of our system across multiple augmented samples of data, and the prediction results showed a mean average precision of 89.44% in five-fold cross validation, thus representing an increase of around 5% over the alternative system. In the real-world test, we collected 10 orthophotographs in various resolutions and areas, and our system successfully detected 711 out of 730 potential disease spots.

Predicting Risks of Severe Windstorm Damage to Southeastern U.S. forests

ContextThe southeastern U.S. experiences tornadoes and severe thunderstorms that can cause economic and ecological damage to forest stands resulting in loss of timber, reduction in short-term carbon sequestration, and increasing forest pests and pathogens. ObjectivesThis project sought to determine landscape-scale patterns of recurring wind damages and their relationships to topographic attributes, overall climatic patterns and soil characteristics in southeastern forests. MethodsWe assembled post-damage assessment data collected since 2012 by the National Oceanic and Atmospheric Administration (NOAA). We utilized a regularized Generalized Additive Model (GAM) framework to identify and select influencing topographic, soil and climate variables and to discriminate between damage levels (broken branches, uprooting, or trunk breakage). Further, we applied a multinomial GAM utilizing the identified variables to generate predictions and interpolated the results to create predictive maps for tree damage. ResultsTerrain characteristics of slope and valley depth, soil characteristics including erodibility factor and bedrock depth, and climatic variables including temperatures and precipitation levels contributed to damage severity for pine trees. In contrast, valley depth and soil pH, along with climactic variables of isothermality and temperature contributed to damage severity for hardwood trees. Areas in the mid-south from Mississippi to Alabama, and portions of central Arkansas and Oklahoma showed increased probabilities of more severe levels of tree damage. ConclusionsOur project identified important soil and climatic predictors of tree damage levels, and areas in the southeastern U.S. that are at greater risk of severe wind damage, with management implications under continuing climate change.

About the causes of pine drying in the Belarusian Polesje

Тhe results of identifying the causes of mass drying of pine trees in the Belarusian Polesje are presented. The object of the study is its modern generations on the former depleted sandy arable land and in natural conditions of growth. It is revealed that in the conditions of groundwater reduction after drainage reclamation in modern climatic conditions, the increase in the inflow of direct solar radiation in the 21st century was the limiting factor for the suppression of the stem productivity of pine, which led to the death of the stand.

Unveiling effects of growth conditions on crown architecture and growth potential of Scots pine trees

Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape as well as about the quality of branches affecting the shape of a crown is, however, still limited. Thus, the aim was to study the crown structure of individual Scots pine trees in forest stands with varying stem densities due to past forest management practices. Furthermore, we wanted to understand how crown and stem attributes as well as tree growth affects stem area at the height of maximum crown diameter (SAHMC), which could be used as a proxy for tree growth potential. We used terrestrial laser scanning (TLS) to generate attributes characterizing crown size and shape. The results showed that increasing stem density decreased Scots pine crown size. TLS provided more detailed attributes for crown characterization compared to traditional field measurements. Furthermore, decreasing stem density increased SAHMC and strong relationships (Spearman correlations >0.5) were found between SAHMC and crown and stem size as well as stem growth. Thus, this study provided quantitative and more comprehensive characterization of Scots pine crowns and their growth potential.

Experimental Tree Mortality Does Not Induce Marsh Transgression in a Chesapeake Bay Low-Lying Coastal Forest

Transgression into adjacent uplands is an important global response of coastal wetlands to accelerated rates of sea level rise. “Ghost forests” mark a signature characteristic of marsh transgression on the landscape, as changes in tidal inundation and salinity cause bordering upland tree mortality, increase light availability, and the emergence of tidal marsh species due to reduced competition. To investigate these mechanisms of the marsh migration process, we conducted a field experiment to simulate a natural disturbance event (e.g., storm-induced flooding) by inducing the death of established trees (coastal loblolly pine, Pinus taeda) at the marsh-upland forest ecotone. After this simulated disturbance in 2014, we monitored changes in vegetation along an elevation gradient in control and treatment areas to determine if disturbance can lead to an ecosystem shift from forested upland to wetland vegetation. Light availability initially increased in the disturbed area, leading to an increase in biodiversity of vegetation with early successional grass and shrub species. However, over the course of this 5-year experiment, there was no increase in inundation in the disturbed areas relative to the control and pine trees recolonized becoming the dominant plant cover in the disturbed study areas. Thus, in the 5 years since the disturbance, there has been no overall shift in species composition toward more hydrophytic vegetation that would be indicative of marsh transgression with the removal of trees. These findings suggest that disturbance is necessary but not sufficient alone for transgression to occur. Unless hydrological characteristics suppress tree re-growth within a period of several years following disturbance, the regenerating trees will shade and outcompete any migrating wetland vegetation species. Our results suggest that complex interactions between disturbance, biotic resistance, and slope help determine the potential for marsh transgression.