Molecular Detection Methods for Forest Pathogens

Critical Reviews in Plant Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 23

Published: April 14, 2025

Language: Английский

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Evaluation of Temporal Trends in Forest Health Status Using Precise Remote Sensing

Drones, Journal Year: 2025, Volume and Issue: 9(5), P. 337 - 337

Published: April 30, 2025

In recent decades, forests have experienced an increasing trend in the number of pest outbreaks worldwide, apparently driven by strong annual variability precipitation, higher air temperatures, and winds. Pest negative ecological, economic, environmental impacts on forest ecosystems, such as reduced biodiversity, carbon sequestration, overall health. Traditional monitoring methods these disturbances, while accurate, are time-consuming limited scope. Remote sensing, particularly UAV (Unmanned Aerial Vehicle)-based technologies, offers a precise cost effective alternative for This study evaluates temporal spatial progression bark beetle damage fir-dominated Zao Mountains, Japan, using RGB imagery DL (Deep Learning) models (YOLO - You Only Look Ones), over four-year period (2021–2024). Trees were classified into six health categories: Healthy, Light Damage, Medium Heavy Dead, Fallen. The results revealed significant decline healthy trees, from 67.4% 2021 to 25.6% 2024, with corresponding increase damaged dead trees. emerged potential early indicator decline. model achieved accuracy 74.9% 82.8%. showed effectiveness detecting severe but highlighted that challenges distinguishing between lightly trees still remain. highlights UAV-based remote sensing health, providing valuable insights targeted management interventions. However, further refinement classification is needed improve accuracy, detection tree categories. approach scalable solution similar ecosystems other subalpine areas Japan world.

Language: Английский

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Integrating indigenous knowledge and culture in sustainable forest management via global environmental policies

Journal of Forest Science, Journal Year: 2024, Volume and Issue: 70(6), P. 265 - 280

Published: June 12, 2024

This research investigates the intricate connection between indigenous knowledge and sustainable forest management, with two main objectives. It seeks to explore outline knowledge, elements, practices that support management (SFM). aims uncover invaluable traditional insights have helped preserve ensure responsible use of ecosystems. Also, it investigated global environmental policies since inception Convention on Biological Diversity (CBD). A narrative review method was employed analyse peer-reviewed contents reports deduce ancestral or wisdom, practices, beliefs from existing studies. The paper extracted data literature scholarly journals. provide useful information for policy-makers, managers, communities, promote SFM development goals related a environment. study found (IK) which includes ethnobotanical plant selection, mixed land use, seed banks, cultural such as sacred groves taboos are some beliefs, can be integrated into international national two-eyed seeing framework (TESF) promote. highlights potential implementing IK SFM. Akwé: Kon Guidelines, United Nations Declaration Rights Indigenous Peoples (UNDRIP), Intergovernmental Science-Policy Platform Biodiversity Ecosystem Services (IPBES), environment recognise people commencement CBD in nineties. recommends, future study, investigating applicability Guidelines projects programs impact lands, forests rivers, people. Framework Climate Change (UNFCCC) Paris Agreement need fully acknowledge supporting role climate change mitigation adaptation solutions, especially Africa, majority world's population constitutes who inhabit healthy standing

Language: Английский

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CustomBottleneck-VGGNet: Advanced tomato leaf disease identification for sustainable agriculture

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110066 - 110066

Published: Feb. 11, 2025

Language: Английский

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Comparison of Artificial Intelligence Algorithms and Remote Sensing for Modeling Pine Bark Beetle Susceptibility in Honduras

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(5), P. 912 - 912

Published: March 5, 2025

The pine bark beetle is a devastating forest pest, causing significant losses worldwide, including 25% of forests in Honduras. This study focuses on Dendroctonus frontalis and Ips spp., which have affected four the seven native species Honduras: Pinus oocarpa, P. caribaea, maximinoi, tecunumanii. Artificial intelligence (AI) an essential tool for developing susceptibility models. However, gaps remain evaluation comparison these algorithms when modeling to outbreaks tropical conifer using Google Earth Engine (GEE). objective this was compare effectiveness three algorithms—random (RF), gradient boosting (GB), maximum entropy (ME)—in constructing models beetles. Data from 5601 pest occurrence sites (2019–2023), 4000 absence samples, set environmental covariates were used, with 70% training 30% validation. Accuracies above 92% obtained RF GB, 85% ME, along robustness area under curve (AUC) up 0.98. revealed seasonal variations susceptibility. Overall, GB outperformed highlighting their implementation as adaptive approaches more effective monitoring system.

Language: Английский

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Emerging Pests and Disease Vectors

Published: Jan. 1, 2025

Language: Английский

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Potential distribution of Biscogniauxia mediterranea and Obolarina persica causal agents of oak charcoal disease in Iran’s Zagros forests

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 2, 2024

Abstract In Iran, native oak species are under threat from episodes of Charcoal Disease, a decline syndrome driven by abiotic stressors (e.g. drought, elevated temperature) and biotic components, Biscogniauxia mediterranea (De Not.) Kuntze Obolarina persica (M. Mirabolfathy). The outbreak is still ongoing the country’s largest ever recorded. Still, factors driving its’ epidemiology in time space poorly known such knowledge urgently needed to develop strategies counteract adverse effects. this study, we developed generic framework based on experimental, machine-learning algorithms spatial analyses for landscape-level prediction charcoal disease outbreaks. Extensive field surveys were conducted during 2013–2015 eight provinces (more than 50 unique counties) Zagros ecoregion. Pathogenic fungi isolated characterized through morphological molecular approaches, their pathogenicity was assessed controlled water stress regimes greenhouse. Further, evaluated set 29 bioclimatic, environmental, host layers modeling incidence data using four well-known machine learning including Generalized Linear Model, Gradient Boosting Random Forest model (RF), Multivariate Adaptive Regression Splines implemented MaxEnt software. Model validation statistics [Area Under Curve (AUC), True Skill Statistics (TSS)], Kappa index used evaluate accuracy each model. Models with TSS above 0.65 prepare an ensemble results showed that among different climate variables, precipitation temperature (Bio18, Bio7, Bio8, bio9) case O. similarly, gsl (growing season length TREELIM, highlighting warming endophytic/pathogenic nature fungus) B. most important influencing variables modeling, while near-surface wind speed (sfcwind) least variant. RF algorithm generates robust predictions (ROC 0.95; 0.77 0.79 MP OP, respectively). Theoretical analysis shows 0.95 0.96; = 0.81 respectively), can efficiently be spatiotemporal distribution. mortality varied ranging 2 14%. Wood-boring beetles association diseased trees determined at 20%. Results deficiency crucial component phenomenon Iran. Northern forests (Ilam, Lorestan, Kermanshah provinces) along southern (Fars Kohgilouyeh va-Boyer Ahmad others endangered areas potential future pandemics disease. Our findings will significantly improve our understanding current situation pave way against pathogenic agents

Language: Английский

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Biogeography and global flows of 100 major alien fungal and fungus‐like oomycete pathogens

Journal of Biogeography, Journal Year: 2023, Volume and Issue: 51(4), P. 599 - 617

Published: Dec. 8, 2023

Abstract Aim Spreading infectious diseases associated with introduced pathogens can have devastating effects on native biota and human livelihoods. We analyse the global distribution of 100 major alien fungal oomycete substantial socio‐economic environmental impacts examine their taxonomy, ecological characteristics, temporal accumulation trajectories, regional hot‐ coldspots taxon richness flows between continents. Location Global. Taxon Alien/cryptogenic fungi fungus‐like oomycetes, pathogenic to plants or animals. Methods To identify over/underrepresented classes phyla, we performed Chi 2 tests independence. describe spatial patterns, calculated region‐wise identified coldspots, defined as residuals after correcting for region area sampling effort via a quasi‐Poisson regression. examined relationship drivers multiple linear regression evaluated potential island effect. Regional first records were pooled over 20‐year periods, links range regions mapped. Results Peronosporomycetes (Oomycota) overrepresented among taxa was positively correlated effort. While no effect found, likely due host limitations, hotspots modification terrestrial land, per capita gross domestic product, temperate tropical forest biomes, orobiomes. increased steeply in recent decades. Europe Northern America recipients, about half originate from Asia. Main Conclusions highlight putative importance anthropogenic drivers, such land use providing conducive environment, contact opportunities susceptible hosts, well economic wealth increasing colonisation pressure. most impacts, possibly partly bias research focus, third show both socio‐economy underscoring maintaining wholescale perspective across natural managed systems.

Language: Английский

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Evaluating the effects of two newly emerging plant pathogens on northern Aotearoa-New Zealand forests using an individual-based model

Ecological Modelling, Journal Year: 2024, Volume and Issue: 500, P. 110938 - 110938

Published: Nov. 20, 2024

Language: Английский

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RNAi-biofungicides: a quantum leap for tree fungal pathogen management

Critical Reviews in Biotechnology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 28

Published: Dec. 8, 2024

Fungal diseases threaten the forest ecosystem, impacting tree health, productivity, and biodiversity. Conventional approaches to combating diseases, such as biological control or fungicides, often reach limits regarding efficacy, resistance, non-target organisms, environmental impact, enforcing alternative approaches. From an ecological standpoint, RNA interference (RNAi) mediated double-stranded (dsRNA)-based strategy can effectively manage fungal pathogens. The RNAi approach explicitly targets suppresses gene expression through a conserved regulatory mechanism. Recently, it has evolved be effective tool in promoting sustainable management bio-fungicides provide efficient eco-friendly disease alternatives using species-specific targeting, minimizing off-target effects. With accessible data on outbreaks, genomic resources, delivery systems, RNAi-based biofungicides promising for managing pathogens forests. However, concerns fate of molecules their potential impact organisms require extensive investigation case-to-case basis. current review critically evaluates feasibility against by delving into methods, persistence, aspects, cost-effectiveness, community acceptance, plausible future protection products.

Language: Английский

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