144,629 research outputs found

    Entomogenic Climate Change

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    Rapidly expanding insect populations, deforestation, and global climate change threaten to destabilize key planetary carbon pools, especially the Earth's forests which link the micro-ecology of insect infestation to climate. To the extent mean temperature increases, insect populations accelerate deforestation. This alters climate via the loss of active carbon sequestration by live trees and increased carbon release from decomposing dead trees. A positive feedback loop can emerge that is self-sustaining--no longer requiring independent climate-change drivers. Current research regimes and insect control strategies are insufficient at present to cope with the present regional scale of insect-caused deforestation, let alone its likely future global scale. Extensive field recordings demonstrate that bioacoustic communication plays a role in infestation dynamics and is likely to be a critical link in the feedback loop. These results open the way to novel detection and monitoring strategies and nontoxic control interventions.Comment: 7 pages, 1 figure; http://cse.ucdavis.edu/~chaos/chaos/pubs/ecc.ht

    The use of multispectral sensing techniques to detect Ponderosa pine trees under stress from insect or pathogenic organisms

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    Multispectral sensing techniques for ground and airborne detection of Ponderosa pine trees under stress from insect or pathogenic organism

    A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring

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    In recent years, the field of computational sustainability has striven to apply artificial intelligence techniques to solve ecological and environmental problems. In ecology, a key issue for the safeguarding of our planet is the monitoring of biodiversity. Automated acoustic recognition of species aims to provide a cost-effective method for biodiversity monitoring. This is particularly appealing for detecting endangered animals with a distinctive call, such as the New Forest cicada. To this end, we pursue a crowdsourcing approach, whereby the millions of visitors to the New Forest, where this insect was historically found, will help to monitor its presence by means of a smartphone app that can detect its mating call. Existing research in the field of acoustic insect detection has typically focused upon the classification of recordings collected from fixed field microphones. Such approaches segment a lengthy audio recording into individual segments of insect activity, which are independently classified using cepstral coefficients extracted from the recording as features. This paper reports on a contrasting approach, whereby we use crowdsourcing to collect recordings via a smartphone app, and present an immediate feedback to the users as to whether an insect has been found. Our classification approach does not remove silent parts of the recording via segmentation, but instead uses the temporal patterns throughout each recording to classify the insects present. We show that our approach can successfully discriminate between the call of the New Forest cicada and similar insects found in the New Forest, and is robust to common types of environment noise. A large scale trial deployment of our smartphone app collected over 6000 reports of insect activity from over 1000 users. Despite the cicada not having been rediscovered in the New Forest, the effectiveness of this approach was confirmed for both the detection algorithm, which successfully identified the same cicada through the app in countries where the same species is still present, and of the crowdsourcing methodology, which collected a vast number of recordings and involved thousands of contributors.</p

    Water trap best for catching Duponchelia

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    How can the harmful Duponchelia insect best be trapped for optimum detection? A water trap was found to be most effectivein a field test. Also, this trap requires little maintenanc

    A Review of Non-Destructive Methods for Detection of Insect Infestation in Fruits and Vegetables

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    Insect damage in fruits and vegetables cause major production and economic losses in the agriculture and food industry worldwide. Monitoring of internal quality and detection of insect infestation in fruits and vegetables is critical for sustainable agriculture. Early detection of an infestation in fruits can facilitate the control of insects and the quarantine operations through proper post-harvest management strategies and can improve productivity. The present review recognizes the need for developing a rapid, cost-effective, and reliable insect infestation monitoring system that would lead to advancements in agriculture and food industry. In this paper, an overview of non-destructive detection insect damages in fruits and vegetables was presented, and the research and applications were discussed. This paper elaborated all of the post-harvest fruit infestation detection methods which are based on the following technologies: optical properties, machine vision technique, sonic properties, magnetic resonance imaging (MRI), thermal imaging, x-ray computed tomography and chemical chromatography. Also, the main challenges and limitations of non-destructive detection methods in the agricultural products quality assessment were also elucidated

    Early detection of insect infestation in stored grain based on head space analysis of volatile compounds

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    Insect infestation is a common problem for stored grain. Insects can cause quantitative losses as kernels are consumed by insects. Also, the appearance and organoleptic properties can be altered through physical damage and contamination by faeces, webbing and body parts of insects, respectively. Therefore, several detection techniques have been developed and applied to stored grains. Some methods demonstrate very high sensitivity but are relatively costly, and thus, are not affordable to the industry; whereas cheaper methods lack detection sensitivity. Consequently, volatile isolation techniques could be an alternative approach to monitor insects in stored grain. Odour detection is a useful tool for monitoring grain quality. It has become part of standard method for grading grain in the United States. Although identification of volatile compounds produced by fungi species has been extensively studied in most types of grains, this has been less done for insect infestation. This paper reviews the literature related to detection of insect infestation in stored wheat by using volatile isolation techniques. Keywords: Grain infestation, Insect detection methods, Volatile isolation techniques, Solid phase microextractio

    In-Field LAMP Detection of Flavescence Dorée Phytoplasma in Crude Extracts of the Scaphoideus titanus Vector

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    One of the most destructive diseases affecting grapevine in Europe is caused by Flavescence Dorée phytoplasma (FDp), which belongs to the 16Sr-V group and is a European Union quarantine pathogen. Although many molecular techniques such as loop-mediated isothermal amplification (LAMP) are widely used for the rapid detection of FDp in infected grapevine plants, there is no developed isothermal amplification assay for FDp detection in the insect vectors that are fundamental for the spread of the disease. For this reason, a simple in-field real-time LAMP protocol was optimized and developed for the specific detection of FDp in the insect vector Scaphoideus titanus. The LAMP assay was optimized to work with crude insect extracts obtained by manually shaking a single insect in a buffer for 5 min. Such a simple, sensitive, specific, economic, and user-friendly LAMP assay allowed the detection of FDp in S. titanus in less than half an hour, directly in the field. The developed insect tissue preparation procedure, combined with the LAMP protocol, promptly revealed the presence of FDp in infected S. titanus directly in the vineyards, allowing for monitoring of the spread of the pathogen in the field and to apply timely strategies required for the mandatory control of this pathogen

    Evaluation of a Novel Rapid Test System for the Detection of Specific IgE to Hymenoptera Venoms

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    Background. The Allergy Lateral Flow Assay (ALFA) is a novel rapid assay for the detection of sIgE to allergens. The objective of this study is the evaluation of ALFA for the detection of sIgE to bee venom (BV) and wasp venom (WV) in insect venom allergic patients. Methods. Specific IgE to BV and WV was analyzed by ALFA, ALLERG-O-LIQ, and ImmunoCAP in 80 insect venom allergic patients and 60 control sera. Sensitivity and specificity of ALFA and correlation of ALFA and ImmunoCAP results were calculated. Results. The sensitivity/specificity of ALFA to the diagnosis was 100%/83% for BV and 82%/97% for WV. For insect venom allergic patients, the Spearman correlation coefficient for ALFA versus ImmunoCAP was 0.79 for BV and 0.80 for WV. However, significant differences in the negative control groups were observed. Conclusion. ALFA represents a simple, robust, and reliable tool for the rapid detection of sIgE to insect venoms

    Genetic Characterization of the Tick-Borne Orbiviruses

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    The International Committee for Taxonomy of Viruses (ICTV) recognizes four species of tick-borne orbiviruses (TBOs): Chenuda virus, Chobar Gorge virus, Wad Medani virus and Great Island virus (genus Orbivirus, family Reoviridae). Nucleotide (nt) and amino acid (aa) sequence comparisons provide a basis for orbivirus detection and classification, however full genome sequence data were only available for the Great Island virus species. We report representative genome-sequences for the three other TBO species (virus isolates: Chenuda virus (CNUV); Chobar Gorge virus (CGV) and Wad Medani virus (WMV)). Phylogenetic comparisons show that TBOs cluster separately from insect-borne orbiviruses (IBOs). CNUV, CGV, WMV and GIV share low level aa/nt identities with other orbiviruses, in 'conserved' Pol, T2 and T13 proteins/genes, identifying them as four distinct virus-species. The TBO genome segment encoding cell attachment, outer capsid protein 1 (OC1), is approximately half the size of the equivalent segment from insect-borne orbiviruses, helping to explain why tick-borne orbiviruses have a ~1 kb smaller genome
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