Patterns, processes and scales shaping invasive pest species dynamics within agricultural landscapes: Modelling the spread of the African citrus psyllid in European lemon orchards

Agricultural Systems, Journal Year: 2025, Volume and Issue: 226, P. 104295 - 104295

Published: March 17, 2025

Language: Английский

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Predicting the potential distribution of Phacellanthus tubiflorus (Orobanchaceae): a modeling approach using MaxEnt and ArcGIS

PeerJ, Journal Year: 2025, Volume and Issue: 13, P. e19291 - e19291

Published: April 28, 2025

Phacellanthus tubiflorus Sieb. et Zucc, a vascular plant species, is believed to possess pharmacological properties including anti-fatigue and immunoenhancement. However, its distribution data limited. Owing the prospective medicinal relevance of this we proposed comprehensive investigation for conservation utilization. In study, aimed scrutinize holistically, ranging from macroscopic microscopic level. Specifically, developed an ecological model using 51 records P. subjected seven environmental conditions. This attained exceptional area under curve (AUC ) value 0.990 with standard deviation 0.004, true skill statistic (TSS) 0.989, indicating potently predictive capacity. Through MaxEnt model, completed systematic depiction niche tubiflorus, revealing primary global distribution. We carried out field surveys in Changbai Mountain region validate model's accuracy conducted observations focusing on phenological attributes highlighting largely subterranean existence. Factors such as seasonality precipitation temperature were found sway distribution, engendering comparably stable acclimation habitats. research contributes repository facilitating subsequent studies species. Integrating botanical approaches, more profound comprehension evaluation species' behavior, survival strategies, associations other populations within specific Furthermore, inclusive approach would assist addressing pivotal issues related species conservation, biodiversity, land development.

Language: Английский

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Utilization of AI – reshaping the future of food safety, agriculture and food security – a critical review

Critical Reviews in Food Science and Nutrition, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 45

Published: Dec. 7, 2024

Artificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there increased interest in embracing applications associated with Intelligence positively contribute food safety. These such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools been established several domains safety early warning outbreaks, risk prediction, detection identification pathogens. Simultaneously, ambition toward establishing sustainable system has motivated adoption cutting-edge technologies strengthen security. Given myriad challenges confronting stakeholders their endeavors safeguard security, emerges promising tool capable crafting holistic management strategies This entails maximizing crop yields, mitigating losses, trimming operational expenses. AI models present notable benefits efficiency, precision, uniformity, automation, pattern identification, accessibility, scalability security endeavors. The escalation global trend adopting alternative protein sources edible insects microalgae source reflects growing recognition need resilient systems address population growth, environmental degradation, insecurity. offers range capabilities enhance production consumption proteins like insects, contributing secure system.

Language: Английский

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Tamarixia radiata global distribution to current and future climate using the climate change experiment (CLIMEX) model

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Feb. 1, 2023

Abstract The phloem-limited bacteria, “ Candidatus Liberibacter asiaticus” and Ca . L. americanus”, are the causal pathogens responsible for Huanglongbing (HLB). Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) is principal vector of these Liberibacter” species. Though Tamarixia radiata Waterston (Hymenoptera: Eulophidae) has been useful in biological control programmes against D. , information on its global distribution remains vague. Using Climate Change Experiment (CLIMEX) model, potential T. under 2050s, 2070s, 2090s Special Report Emissions Scenarios A1B A2 was defined globally. results showed that habitat suitability covered Africa, Asia, Europe, Oceania, Americas. model predicted climate suitable areas beyond presently known native non-native areas. new locations to have included parts Europe Oceania. Under different change scenarios, contraction high (EI > 30) from 2050s 2090s. Nevertheless, maps created using CLIMEX may be helpful search release regions.

Language: Английский

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First report on the presence of huanglongbing vectors (Diaphorina citri and Trioza erytreae) in Ghana

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: July 13, 2023

As significant threats to global citrus production, Diaphorina citri (Kuwayama; Hemiptera: Psyllidae) and Trioza erytreae (Del Guercio; Triozidae) have caused considerable losses trees globally. vectors "Candidatus Liberibacter asiaticus" "Ca. L. americanus", whereas T. transmits africanus" asiaticus", the pathogens responsible for greening disease or Huanglongbing (HLB). Though HLB is a destructive of wherever it occurs, information on occurrence geographical distribution its in Africa limited. In recent surveys determine if are present Ghana, we observed eggs, nymphs, adults insects suspected be D. erytreae. Using morphological traits DNA analyses, identity was confirmed Individuals were examined using qPCR CLaf, CLam, CLas, but none them tested positive any species. Herein report, first time, presence Ghana (West Africa). We discuss implications this new threat industry formulate appropriate management strategies.

Language: Английский

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Species distribution models predicting climate suitability for the psyllid Trioza erytreae, vector of citrus greening disease

Crop Protection, Journal Year: 2023, Volume and Issue: 168, P. 106228 - 106228

Published: March 7, 2023

The African citrus psyllid, Trioza erytreae (Hemiptera: Triozidae), is a vector of greening (Huanglonbing - HLB), bacterial disease caused by Candidatus liberibacter spp. Native to Africa, T. was detected in the Canary Islands and Madeira early 2000s then northwestern Spain 2014. Since then, has become established along Atlantic coastal areas Iberian Peninsula. Therefore, an accurate assessment potential long-term establishment major citrus-growing regions Europe world urgently needed design adapted control strategies. I calibrated correlative species distribution models understand bioclimatic characteristics that determine erytreae, assess climatic suitability world's for psyllid under current future climate conditions. using only data from Africa (its native range), Islands, Madeira, evaluated them available invaded area continental Europe. This approach aims avoid spurious good measures model accuracy arising spatial autocorrelation between calibration evaluation datasets. identify mild summer winter temperatures high levels precipitation as optimal conditions establishment, consistent with its physiology. In Europe, predict Peninsula highly climatically suitable, pattern corresponds exactly currently psyllid. Models most important are, will remain future, poorly except case physiological adjustments. These results are crucial appropriate pest management strategies timely where recently been detected.

Language: Английский

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Estimating the Climate Niche of Sclerotinia sclerotiorum Using Maximum Entropy Modeling

Journal of Fungi, Journal Year: 2023, Volume and Issue: 9(9), P. 892 - 892

Published: Aug. 31, 2023

Sclerotinia sclerotiorum, a fungal pathogen, causes world-wide crop losses and additional disease management strategies are needed. Modeling the climate niche of this fungus may offer tool for selection biological control organisms cultural methods control. Maxent, modeling technique, was used to characterize fungus. The technique requires occurrence data, bioclimatic data layers, geospatial analysis. A cross-correlation performed with ArcGIS 10.8.1, reduce nineteen variables (WorldClim) nine variables. model results were evaluated by AUC (area under curve). final created random seed procedure Maxent gave an average 0.935 difference -0.008. most critical included annual precipitation (importance: 14.1%) range 450 mm 2500 mean temperature coldest quarter 55.6%) -16 °C 24 °C, which contributed model. habitat suitability map generated in 10.8.1 from validated comparing another dataset. Z-Score statistical test confirmed no significant differences between two datasets all areas.

Language: Английский

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The African citrus psyllid Trioza erytreae (Hemiptera: Triozidae): Biology, management, and its role as a vector of huanglongbing

Crop Protection, Journal Year: 2023, Volume and Issue: 172, P. 106348 - 106348

Published: July 13, 2023

Language: Английский

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A dual track deep fusion network for citrus disease classification using group shuffle depthwise feature pyramid and Swin transformer

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102302 - 102302

Published: Sept. 10, 2023

Language: Английский

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Machine learning for pest detection and infestation prediction: A comprehensive review

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(5)

Published: July 14, 2024

Abstract Pests pose a major danger to variety of industries, including agriculture, public health, and ecosystems. Fast precise pest detection, as well the ability predict infestations, are required for effective management tactics. This paper provides comprehensive literature review on this subject provide an overview state research detection infestation prediction. The investigates presents background information necessity control difficulty in recognizing pests forecasting. Several strategies, approaches data collection, modeling, assessment models, reviewed described. authors examine various methods involving utilization convolutional neural networks several object architectures categorized broadly into one‐stage two‐stage algorithms. Methods predicting infestations that involve regression, classification, time series forecasting also thoroughly investigated. challenges underlined, issues with quality, feature selection, model interpretability. report indicates limitations prediction intriguing topics further same. findings demonstrate how Artificial Intelligence, Computer Vision, Internet Things have been applied Pest Detection Infestation Prediction. serves base surveying summarizing utilized task (an problem) (a its recommendations serve platform future study development solutions. article is under: Application Areas > Health Care Technologies Machine Learning Prediction

Language: Английский

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