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Abstract: Brown spot needle blight (BSNB), caused by Lecanosticta acicola (Thüm.) Syd., is an emerging forest disease of Pinus species originating from North America and introduced to Europe and Asia. Severity and spread of the disease has increased in the last two decades in North America and Europe as a response to climate change. No modeling work on spread, severity, climatic suitability, or potential distribution has been done for this important emerging pathogen. This study utilizes a global dataset of 2,970 independent observations of L. acicola presence and absence from the geodatabase, together with Pinus spp. distribution data and 44 independent climatic and environmental variables. The objectives were to (1) identify which bioclimatic and environmental variables are most influential in the distribution of L. acicola; (2) compare four modeling approaches to determine which modeling method best fits the data; (3) examine the realized distribution of the pathogen under climatic conditions in the reference period (1971–2000); and (4) predict the potential future global distribution of the pathogen under various climate change scenarios. These objectives were achieved using a species distribution modeling. Four modeling approaches were tested: regression-based model, individual classification trees, bagging with three different base learners, and random forest. Altogether, eight models were developed. An ensemble of the three best models was used to make predictions for the potential distribution of L. acicola: bagging with random tree, bagging with logistic model trees, and random forest. Performance of the model ensemble was very good, with high precision (0.87) and very high AUC (0.94). The potential distribution of L. acicola was computed for five global climate models (GCM) and three combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (SSP-RCP): SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5. The results of the five GCMs were averaged on combined SSP-RCP (median) per 30-year period. Eight of 44 studied factors determined as most important in explaining L. acicola distribution were included in the models: mean diurnal temperature range, mean temperature of wettest quarter, precipitation of warmest quarter, precipitation seasonality, moisture in upper portion of soil column of wettest quarter, surface downwelling longwave radiation of driest quarter, surface downwelling shortwave radiation of warmest quarter and elevation. The actual distribution of L. acicola in the reference period 1971–2000 covered 5.9% of Pinus spp. area globally. However, the model ensemble predicted potential distribution of L. acicola to cover an average of 58.2% of Pinus species global cover in the reference period. Different climate change scenarios (five GCMs, three SSP-RCPs) showed a positive trend in possible range expansion of L. acicola for the period 1971–2100. The average model predictions toward the end of the century showed the potential distribution of L. acicola rising to 62.2, 61.9, 60.3% of Pinus spp. area for SSP1-RCP2.6, SSP2-RCP4.5, SSP5-RCP8.5, respectively. However, the 95% confidence interval encompassed 35.7–82.3% of global Pinus spp. area in the period 1971–2000 and 33.6–85.8% in the period 2071–2100. It was found that SSP-RCPs had a little effect on variability of BSNB potential distribution (60.3–62.2% in the period 2071–2100 for medium prediction). In contrast, GCMs had vast impact on the potential distribution of L. acicola (33.6–85.8% of global pines area). The maps of potential distribution of BSNB will assist forest managers in considering the risk of BSNB. The results will allow practitioners and policymakers to focus surveillance methods and implement appropriate management plans.
Keywords: brown spot needle blight, BSNB, pines, species distribution model, climate change, biosecurity
Published in DiRROS: 02.08.2023; Views: 325; Downloads: 205
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Abstract: We investigated several aspects of windthrow that are relevant to our understanding and management of forest ecosystems. As an example, we used an extreme event in December 2017, when the strongest storm in recent history occurred in the Slovenian Dinaric High Karst. We examined influential factors such as soil properties, wind speed, precipitation and ecological consequences for the affected forests. Soil properties were measured around standing and fallen silver fir trees at all three research sites. Tree species composition in the regeneration was observed on plots with chemical and acoustic ungulate deterrents and on control plots without deterrents. Economic estimates of yield loss due to damage were calculated at the national level. A model of the potential threat from windthrow was also developed based on data collected from windthrow events and meteorological data over the past 20 years. Our results indicate that soil depth and mineral fraction depth were similar at sites with and without damaged trees and were not the determining factors for tree toppling. Plots with acoustic deterrents showed the most effective regeneration development, the least decline in silver fir and the greatest increase in noble hardwood seedlings, while plots with chemical deterrents showed the least browsing damage. The estimated economic loss of €16.1 million is 6.6% less than the harvest under normal conditions. The economic loss was relatively low due to the nature of the storm, with the predominant type of damage being uprooted trees with no damaged trunks. The windthrow hazard model revealed that a large number of consecutive events with strong winds in each section weakened the stand, which was subsequently knocked down during the next extreme wind and rainfall event.
Keywords: natural forest regeneration, windthrow, economic loss, browsing, ungulate deterrents, forest soil properties, potential threat model
Published in DiRROS: 27.06.2023; Views: 1118; Downloads: 523
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Abstract: Finite element (FE) model updating of bridges is based on the measured modal parameters and less frequently on the measured structural response under a known load. Until recently, the FE model updating did not consider strain measurements from sensors installed for weighing vehicles with bridge weigh-in-motion (B-WIM) systems. A 50-year-old multi-span concrete highway viaduct, renovated between 2017 and 2019, was equipped with continuous monitoring system with over 200 sensors, and a B-WIM system. In the most heavily instrumented span, the maximum measured longitudinal strains induced by the full-speed calibration vehicle passages were compared with the modelled strains. Based on the sensitivity study results, three variables that affected its overall stiffness were updated: Young’s modulus adjustment factor of all structural elements, and two anchorage reduction factors that considered the interaction between the superstructure and non-structural elements. The analysis confirmed the importance of the initial manual FE model updating to correctly reflect the non-structural elements during the automatic nonlinear optimisation. It also demonstrated a successful use of pseudo-static B-WIM loading data during the model updating process and the potential to extend the proposed approach to using random B-WIM-weighed vehicles for FE model updating and long-term monitoring of structural parameters and load-dependent phenomena.
Keywords: monitoring, bridge, viaduct, bridge weigh-in-motion (B-WIM), structural health monitoring (SHM), finite element (FE), calibration, model updating
Published in DiRROS: 29.05.2023; Views: 392; Downloads: 213
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Keywords: gozdovi, varstvo gozdov, navadna smreka, Picea abies, sanitarni posek, napoved, prognoza, ogroženost, model, smrekovi podlubniki, Ips typographus, Pityogenes chalcographus
Published in DiRROS: 24.03.2023; Views: 434; Downloads: 174
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Keywords: gozdovi, zdravje gozdov, sanitarni posek, napovedi, podlubniki, Slovenija, navadna smreka, Picea abies, ogroženost, model, validacija, zmogljivost, zanesljivost, točnost, natančnost, AUC, občutljivost, specifičnost
Published in DiRROS: 14.12.2022; Views: 417; Downloads: 220
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