Evaluating scent detection dogs as a tool to detect pathogenic Phytophthora species (original) (raw)
Proceedings of the National Academy of Sciences
Early detection and rapid response are crucial to avoid severe epidemics of exotic pathogens. However, most detection methods (molecular, serological, chemical) are logistically limited for large-scale survey of outbreaks due to intrinsic sampling issues and laboratory throughput. Evaluation of 10 canines trained for detection of a severe exotic phytobacterial arboreal pathogen, Candidatus Liberibacter asiaticus (CLas), demonstrated 0.9905 accuracy, 0.8579 sensitivity, and 0.9961 specificity. In a longitudinal study, cryptic CLas infections that remained subclinical visually were detected within 2 wk postinfection compared with 1 to 32 mo for qPCR. When allowed to interrogate a diverse range of in vivo pathogens infecting an international citrus pathogen collection, canines only reacted to Liberibacter pathogens of citrus and not to other bacterial, viral, or spiroplasma pathogens. Canines trained to detect CLas-infected citrus also alerted on CLas-infected tobacco and periwinkle, C...
Entropy
For millennia humans have benefitted from application of the acute canine sense of smell to hunt, track and find targets of importance. In this report, canines were evaluated for their ability to detect the severe exotic phytobacterial arboreal pathogen Xanthomonas citri pv. citri (Xcc), which is the causal agent of Asiatic citrus canker (Acc). Since Xcc causes only local lesions, infections are non-systemic, limiting the use of serological and molecular diagnostic tools for field-level detection. This necessitates reliance on human visual surveys for Acc symptoms, which is highly inefficient at low disease incidence, and thus for early detection. In simulated orchards the overall combined performance metrics for a pair of canines were 0.9856, 0.9974, 0.9257 and 0.9970, for sensitivity, specificity, precision, and accuracy, respectively, with 1–2 s/tree detection time. Detection of trace Xcc infections on commercial packinghouse fruit resulted in 0.7313, 0.9947, 0.8750, and 0.9821 f...
Detector dogs could be trained to find invasive insect pests at borders before they establish in new areas. However, without access to the live insects themselves, odor training aids are needed to condition dogs to their scent. This proof-of-concept study assessed 2 potential training aids for insect detection: a scent extract and dead specimens of the target species. Using Musgraveia sulciventris (Hemiptera: Tessaratomidae) as an experimental model, gas chromatography-mass spectrometry (GC-MS) analyses were carried out to compare the chemical headspaces that make up the odors of live specimens and these 2 training aids. This was then followed by canine scentdetection testing to investigate biosecurity detector dogs' (n = 4) responses to training in an ecologically valid context. Both the scent extract and the dead specimens shared the majority of their volatile organic compounds (VOCs) with live insects. Of the dogs trained with scent extract (n = 2), both were able to detect the live insects accurately, and of those trained with dead specimens (n = 2), one detected the live insects accurately. These findings lend support for these training aids as odor-proxies for live insects-particularly scent extract, which is a relatively novel product with the potential for broad application to facilitate and improve insect-detection training.
Quantifying the Sensitivity of Scent Detection Dogs To Identify Fecal Contamination on Raw Produce
Journal of Food Protection, 2014
Consumption of raw produce commodities has been associated with foodborne outbreaks in the United States. In a recent Centers for Disease Control and Prevention report outlining the incidence of food-related outbreaks from 1998 to 2008, produce of all kinds were implicated in 46% of illnesses and 23% of deaths. Methods that quickly identify fecal contamination of foods, including produce, will allow prioritization of samples for testing during investigations and perhaps decrease the time required to identify specific brands or lots. We conducted a series of trials to characterize the sensitivity and specificity of scent detection dogs to accurately identify fecal contamination on raw agricultural commodities (romaine lettuce, spinach, cilantro, and roma tomatoes). Both indirect and direct methods of detection were evaluated. For the indirect detection method, two dogs were trained to detect contamination on gauze pads previously exposed to produce contaminated with feces. For the di...
Defining the Characteristics of Successful Biosecurity Scent Detection Dogs
Animals
To perform their role effectively, scent detection dogs require certain characteristics. Identifying these characteristics will inform the selection of prospective dogs and preferred approaches to their training. The current study drew upon the perspectives of industry stakeholders to identify the behavioural traits considered relevant for detection dogs in biosecurity screening roles. Dog handlers, trainers, and supervisors (n = 25) in Australian biosecurity operations participated in focus group interviews to determine the perceived characteristics that, in their experience, influence detection performance. Their descriptions were used to create a questionnaire which was then administered to handlers to assess the working behaviours of current biosecurity dogs. Responses were collected for 88% of the operational dogs (n = 36). An exploratory factor analysis revealed seven tentative dimensions: search motivation, emotional stability, search arousal, food motivation, play motivation...
Sensors
Compared with traditional gas chromatography–mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors’ response to the odors’ exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models’ performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the tw...
Identification and Detection of Phytophthora: Reviewing Our Progress, Identifying Our Needs
Plant Disease, 2012
With the increased attention given to the genus Phytophthora in the last decade in response to the ecological and economic impact of several invasive species (such as P. ramorum, P. kernoviae, and P. alni), there has been a significant increase in the number of described species. In part, this is due to the extensive surveys in historically underexplored ecosystems (e.g., forest and stream ecosystems) undertaken to determine the spread of invasive species and the involvement of Phytophthora species in forest decline worldwide (e.g., oak decline). The past decade has seen an approximate doubling in the number of described species within the genus Phytophthora, and the number will likely continue to increase as more surveys are completed and greater attention is devoted to clarifying phylogenetic relationships and delineating boundaries in species complexes. The development of molecular resources, the availability of credible sequence databases to simplify identification of new specie...
Using synthetic semiochemicals to train canines to detect bark beetle–infested trees
Annals of Forest Science
& Key message The dog detection allows timely removal by sanitation logging of first beetle-attacked trees before offspring emergence, preventing local beetle increases. Detection dogs rapidly learned responding to synthetic bark beetle pheromone components, with known chemical titres, allowing search training during winter in laboratory and field. Dogs trained on synthetics detected naturally attacked trees in summer at a distance of > 100 m. & Context An early detection of first beetle-attacked trees would allow timely sanitation felling before offspring emergence, curbing local beetle increase. & Aims We tested if detection dogs, trained off-season on synthetic pheromone components from Ips typographus, could locate naturally bark beetle-infested spruce trees. & Methods Indoor training allowed dogs to discriminate between the infestation odours (target) and natural odours (non-target) from the forest. Odour stimuli were shown by chemical analysis to be bioactive at extremely low-levels released (< 10 −4 ng/ 15 min) in the laboratory. & Results Detection dogs, trained to recognise four different synthetic pheromone compounds in the wintertime, were able to detect naturally infested spruce trees unknown to humans the following summer. The dog-handler pairs were able to detect an infested spruce tree from the first hours of beetle attack until several weeks after first attack, long before discolouration of the crown. Trained sniffer dogs detected infested spruce trees out to ≥ 100 m, as measured by GPS-collar tracks. & Conclusion Dog-handler pairs appear to be more efficient than humans alone in timely detecting bark beetle infestations due to the canine's ability to cover a greater area and detect by olfaction infestations from a far longer distance than can humans.
2014
The brown spruce longhorn beetle (BSLB), Tetropium fuscum, native to Europe and Asia, is an alien wood-boring insect of quarantine significance with established populations in Atlantic Canada. In their non-native range, the BSLB kills spruce trees, primarily red spruce, Picea rubens, and is therefore a potential threat to the ecological integrity of forest habitats and the availability of resources for pulp and paper in North America. Collective efforts have been undertaken by the Canadian Forest Service (CFS) and the Canadian Food Inspection Agency (CFIA), including slow-the-spread programs to eradicate this invasive pest. Identification of infested trees is currently achieved by human visual ground surveys for characteristic signs and symptoms of tree trunks or pruned branches. Although usually reliable, false positives and misses occur, which lead to wasted human resources and unnecessary tree removal. Early detection of infestation is crucial to the management and eradication of this invasive insect. The primary objective of this proof-of-concept study is to determine whether sniffer dogs are able to be trained to detect the BSLB in laboratory conditions. The application of trained sniffer dogs is anticipated to improve current BSLB management strategies by having a high positive predictive power as well as a low proportion of false alarms and misses regardless of the presence of physical/visual symptoms of infestation. Three volunteer dogs were selected based on their motivation to work and ability to detect low saliency stimuli. Dogs were trained in psychophysical matching procedures of BSLB larvae. All the dogs have the ability to detect the larvae up to 100% accuracy in the presence of ecologically valid distracting stimuli. In addition to providing a potential novel avenue for forest pest management, the researchers anticipate applying the methodology to other invasive insect species, such as the emerald ash borer, Agrilus planipennis.