Habitat and stand factors related to spatial dynamics of Norway spruce dieback driven by Ips typographus (L.) in the Białowieża Forest District

Kamińska, Agnieszka; Lisiewicz, Maciej; Kraszewski, Bartłomiej; Stereńczak, Krzysztof

doi:10.1016/j.foreco.2020.118432

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…However, we currently lack a comprehensive explanation for the decrease in tree mortality at greater distances. One plausible explanation is the natural spatial autocorrelation of I. typographus attacks [47], whereby the beetles tend to target trees in close proximity to previous infestations. As the distance from these infested trees increases, the probability of attack decreases, which corresponds to the observed pattern of decline beyond 60 m. This phenomenon has also been shown in previous studies by Schiebe et al (21).…”

Section: Effectiveness Of Anti-attractant Mixturesmentioning

confidence: 99%

Spruce Protection against Ips typographus with Anti-Attractant Blend of Tree-Based Semiochemicals: From Small Experimental Plots to Stand Scales

Jakuš,

Trubin,

Singh

et al. 2024

Forests

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Tree-killing bark beetles require rapid management, such as anti-attractants, to stop the enlargement of attack hot-spots. We tested two newer anti-attractant blends, both without verbenone and one with the addition of trans-4-thujanol, in traps against standard pheromone baits for inhibition of catch. Both blends provided effective catch reduction (>95%). We also tested these anti-attractant blends in tree protection experiments for two years. We had experimental plots with a center of an anti-attractant protected tree zone, with no traditional control area, but we followed tree kills in 10 m wide concentric rings to 100 m. In 2020, we had 12 plots, and 9 plots in 2021. Monitoring by low-strength pheromone traps followed beetle flight averaging 300/trap during the shorter period, August 2020, and 5000/trap during the longer period, May to August 2021. The blends of anti-attractants were 100% effective in avoiding tree mortality in both treated trees and their surroundings. There were no bark beetle attacks on any treated trees, and there was zero tree mortality up to 19 m in 2020, and up to 30 m in 2021, thus full protection to circa 20 m. The density of killed trees then increased from close to zero, over 20 to 50 m, reaching a level of ca 30 (trees/ha) then declined. The spatial pattern of tree mortality on our experimental plots was highly heterogeneous and individual 10m-ring data points on tree kill density could not be statistically separated. In contrast, a non-linear regression model showed a continuous increase of attacks over the distance from the center to a peak ca 60m, followed by a decline. This model agrees partly with the only similar study in the literature, but importantly, it does not give a peak of kill density at distances between the first and second rings close to treatment zones. Such patterns of close-quarter kills have been observed as a “switching” of attack in this and other scolytid systems manipulated by anti-attractants, but not in the present study, likely due to the elimination of verbenone from our blends.

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Section: Effectiveness Of Anti-attractant Mixturesmentioning

confidence: 99%

Spruce Protection against Ips typographus with Anti-Attractant Blend of Tree-Based Semiochemicals: From Small Experimental Plots to Stand Scales

Jakuš,

Trubin,

Singh

et al. 2024

Forests

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“…Another way is to model the spatial patterns by using data that was remotely obtained and was relevant to the outbreak. For example, some works used topographic features [14], stand attributes [15], and landscape patterns [16,17] in the modeling of PWN spread. The justification for this is that if the response of PWN outbreak is related to those factors, a more accurate PWN spread can be estimated when the modeling function includes these factors and consider their effects.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

et al. 2021

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Pine wood nematode (PWN), Bursaphelenchus xyophilus, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread.

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“…Many studies have dealt with the issue of tree infestation by beetles of the genus Scolytinae (bark beetle). The attention of scientists has focused mainly on tree attacks [1][2][3], chemical control and communication [4][5][6][7][8], external factors affecting population size [9,10], and bark beetle development in the larval stage [11]. All these findings have been used not only in the practical application of forest management, but also as potential sources of information for predicting the development of the bark beetle population in individual areas [12].…”

Section: Introductionmentioning

confidence: 99%

What Happens to Wood after a Tree Is Attacked by a Bark Beetle?

Hýsek

Löwe

Turčáni

2021

Forests

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Advancing climate change is affecting the health and vitality of forests in many parts of the world. Europe is currently facing spruce bark beetle outbreaks, which are most often caused by wind disturbances, hot summers, or lack of rainfall and are having a massive economic impact on the forestry sector. The aim of this research article was to summarize current scientific knowledge about the structure and physical and mechanical properties of wood from bark beetle-attacked trees. Spruce stands are attacked by a number of beetles, of which Ips typographus is the most common and widespread in Central Europe. When attacking a tree, bark beetles introduce ophiostomatoid fungi into the tree, which then have a greater effect on the properties of the wood than the beetles themselves. Fungal hyphae grow through the lumina of wood cells and spread between individual cells through pits. Both white rot and brown rot fungi are associated with enzymatic degradation of lignin or holocellulose, which is subsequently reflected in the change of the physical and mechanical properties of wood. Wood-decay fungi that colonize wood after infestation of a tree with bark beetles can cause significant changes in the structure and properties of the wood, and these changes are predominantly negative, in the form of reducing modulus of rupture, modulus of elasticity, discolouration, or, over time, weight loss. In certain specific examples, a reduction in energy consumption for the production of wood particles from beetle-attacked trees, or an increase in surface free energy due to wood infestation by staining fungi in order to achieve better adhesion of paints or glues, can be evaluated positively.

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Habitat and stand factors related to spatial dynamics of Norway spruce dieback driven by Ips typographus (L.) in the Białowieża Forest District

Cited by 8 publications

References 39 publications

Spruce Protection against Ips typographus with Anti-Attractant Blend of Tree-Based Semiochemicals: From Small Experimental Plots to Stand Scales

Spruce Protection against Ips typographus with Anti-Attractant Blend of Tree-Based Semiochemicals: From Small Experimental Plots to Stand Scales

Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

What Happens to Wood after a Tree Is Attacked by a Bark Beetle?

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