Canine Olfactory Detection of a Non-Systemic Phytobacterial Citrus Pathogen of International Quarantine Significance

Abstract

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 for the same performance metrics across a range of cartons with 0-10% Xcc-infected fruit despite the noisy, hot and potentially distracting environment. In orchards, the sensitivity of canines increased with lesion incidence, whereas the specificity and overall accuracy was >0.99 across all incidence levels; i.e., false positive rates were uniformly low. Canines also alerted to a range of 1-12-week-old infections with equal accuracy. When trained to either Xcc-infected trees or Xcc axenic cultures, canines inherently detected the homologous and heterologous targets, suggesting they can detect Xcc directly rather than only volatiles produced by the host following infection. Canines were able to detect the Xcc scent signature at very low concentrations (10,000× less than 1 bacterial cell per sample), which implies that the scent signature is composed of bacterial cell volatile organic compound constituents or exudates that occur at concentrations many fold that of the bacterial cells. The results imply that canines can be trained as viable early detectors of Xcc and deployed across citrus orchards, packinghouses, and nurseries.

Keywords: Asiatic citrus canker; deployment; direct assay; early detection; field diagnostic; information theory; latent class; scent signature.

Figure 1

Initial training of canines for detection of the phytobacterial arboreal pathogen Xanthomonas citri pv. citri (Xcc), the causal agent of Asiatic citrus canker (Acc). (A) Xcc-infected red grapefruit fruit and (B) foliage. (C) Scent Transfer Unit (STU) used to draw in Xcc volatiles and deposit on cotton scent collection pad. (D) Canine “Kimba” training by interrogating a row of metal cans containing Xcc-positive and negative scent pads. (E,F) Detector canine NDD-1 and NDD-3 alerting on boxes containing Xcc-infected foliage at the USDA, APHIS, National Detector Dog Training Center.

Figure 2

Canine detection of Xanthomonas citri pv. citri (Xcc) in simulated and commercial orchards. (A) Xcc-infected, potted red grapefruit inserted into ground—Xcc lesions indicated by red arrows. Detector canine “Juice”—(B) interrogating, and (C) alerting on Xcc-infected trees. Detector canine “Bady”—(D) interrogating, and (E) alerting on Xcc-infected trees. (F) “Juice” alerting on Xcc-infected grapefruit tree in commercial orchard. (G) Sample of three Xcc-infected grapefruit leaves from commercial orchard identified by “Juice”—note multiple small brown Xcc lesions surrounded by chlorotic halos.

Figure 3

Canine detection of abscised Xcc-infected grapefruit leaves over time. (A) Mixed Xcc-infected and non-infected leaves in wire mesh cage, (B) close up of leaves in wire mesh decaying. Canine “Bady”—(C) interrogating, and (D) alerting on wire mesh cages with decaying Xcc-infected leaves. (E) Commercially packed grapefruit in cardboard carton with top layer of fruit removed to show Xcc-infected fruits—red arrows indicate infected fruit with Xcc lesions. (F) Grid of 100 cartons of commercial packed red grapefruit arrayed on packinghouse floor for canine interrogation; 1–6 cartons contain Xcc-infected fruits—positions of Xcc-infected cartons randomized between trials.

Figure 4

Latent class metrics for the effect of incidence of Xcc lesions on canine detection. The data demonstrate a training effect where canine detection of Xcc-infected trees (sensitivity) significantly improves between the first and second tests, which also improves slightly the overall accuracy metric. In essence, the canines learn the “game” of detecting Xcc-infected trees when presented with a grid imposed by the experimental design and become more proficient at detection over time.

Figure 5

Latent class metrics for canine detection of Xcc-infections of increasing age. There was no relationship of lesion age on canine detection of Xcc-infected trees. However, the data demonstrate a training effect for canine detection of Xcc-infected trees (sensitivity) which significantly improves between the first (1) and second (2) tests as canines learn the “game” imposed by the experimental design and become more proficient at detection. Acc= Accuracy.

Figure 6

Effect of lesion incidence on false negative canine detections. The data demonstrate a general erosion of canine detection of Xcc-infected trees (sensitivity) as the incidence of infection within individual trees increases. As the scent signature becomes stronger due to heavy infection in some trees, canines begin to false alert on nearby trees because they acquire the scent farther away from the true source.

Figure 7

Effect of Xcc lesion incidence on overall accuracy, sensitivity, and specificity of canine detections. The data demonstrate a general improvement in the sensitivity of canine detection as training experience for both canines was accumulated over an increasing number of trials, whereas overall accuracy was high throughout and improved only slightly over accumulated trials and specificity remained high and stable. We use the overall results for each animal displayed in Table 6 to illustrate the diagnostic performance of the canines Bady and Maxi in an information theoretic framework. Figure 8 shows the results of this exercise.

Figure 8

(A) Likelihood ratio plot for canines Bady and Maxi, based on average performance over a range of pathogen prevalence values in citrus canker detection trials. The axes are the same as those for a receiver operator characteristic (ROC) curve, each canine being represented by a single point. In general, the closer the point for the animals to the upper left corner with TPP = 1, FPP = 0, the better the overall diagnostic performance. The gradient of the solid line section is the positive likelihood ratio for cases for each canine. The gradient of the dashed section is the negative likelihood ratio. (B) A predictive value leaf plot for Bady and Maxi based on the likelihood ratio values displayed panel (A). The plot displays the relationship between possible disease prevalence (or prior in a Bayesian framework) and the possible post-diagnostic probability of disease given either positive or negative diagnostic outcomes. The canines have very similar positive alert performance, but they differ in the information they provide in negative alerts. In general, a negative alert by Maxi provides more information than one by Bady. For both canines, positive alerts result in a high post-test probability of disease even at low prior disease values. (C) A relative entropy “loop” plot for each animal based on the same likelihood ratios. For each animal, disease prevalence increases clockwise around the loop which shows the expected information supplied (in bits) for a positive vs. negative alert at each possible disease prevalence between 0 and 1 in steps of 0.0001. In effect, the loop plot shows the information gain from alerts corresponding to the change in probable disease prevalence following diagnosis displayed in the leaf plot in panel (B).

Figure 9

Effect of incidence (proportion) of commercial fruit boxes containing Xcc-infected grapefruit on false negative canine detections. The data demonstrate a general erosion of canine detection of Xcc-positive boxes (sensitivity) as the incidence of infected boxes increases within the grid in the packinghouse. As the scent signature becomes more prevalent within the test grid commensurate with the number of boxes containing Xcc-infected fruit, canines begin to false alert on nearby boxes in close proximity to boxes with actual infected fruit because the canines acquire the scent farther away for the true source.

Figure 10

Canine versus human visual detection of Xcc-infection in commercial citrus orchards in Indian River County, Florida. (A) Orchard 1—Mature 42-year-old red grapefruit on sour orange rootstock, (B) Orchard 2—7-year-old red grapefruit.

Figure 11

Spatial heterogeneity analysis of canine detection errors for trials of (A) lesion incidence, (B) lesion age, (C) packinghouse, and (D) combined data A through C. In all three trials there was a greater number of false negative (FN) than false positive (FP) errors. All trials had a greater prevalence of FN errors and errors were more prevalent at shorter distances from a true positive (TP). Distance is presented as multiples of the distance between plants or cartons (packinghouse) in the grid, i.e., 3.048 m (10 ft) within and between rows.