Conservation detection dogs: A critical review of efficacy and methodology

Conservation detection dogs (CDD) use their exceptional olfactory abilities to assist a wide range of conservation projects through the detection of target specimens or species. CDD are generally quicker, can cover wider areas and find more samples than humans and other analytical tools. However, their efficacy varies between studies; methodological and procedural standardisation in the field is lacking. Considering the cost of deploying a CDD team and the limited financial resources within conservation, it is vital that their performance is quantified and reliable. This review aims to summarise what is currently known about the use of scent detection dogs in conservation and elucidate which factors affect efficacy. We describe the efficacy of CDD across species and situational contexts like training and fieldwork. Reported sensitivities (i.e. the proportion of target samples found out of total available) ranged from 23.8% to 100% and precision rates (i.e. proportion of alerts that are true positives) from 27% to 100%. CDD are consistently shown to be better than other techniques, but performance varies substantially across the literature. There is no consistent difference in efficacy between training, testing and fieldwork, hence we need to understand the factors affecting this. We highlight the key variables that can alter CDD performance. External effects include target odour, training methods, sample management, search methodology, environment and the CDD handler. Internal effects include dog breed, personality, diet, age and health. Unfortunately, much of the research fails to provide adequate information on the dogs, handlers, training, experience and samples. This results in an inability to determine precisely why an individual study has high or low efficacy. It is clear that CDDs can be effective and applied to possibly limitless conservation scenarios, but moving forward researchers must provide more consistent and detailed methodologies so that comparisons can be conducted, results are more easily replicated and progress can be made in standardising CDD work.

Comparing narcotics detection canine accuracy across breeds

Investigations of detection canine performance across breeds has yielded mixed results, often reporting behavioral differences observed by researchers or reported by handlers through surveys. The present study tested 34 narcotics detection canines; 25 Belgian Malinois and 9 German Shepherds to determine if there were any differences between the breeds in overall rate of detection accuracy and the number of false alerts in searches. As previously determined, results of the present study support the continued use of narcotic detection canines for law enforcement agencies (i.e., positive alert percentage >90%; false alert percentage <10%). The results indicate that Belgian Malinois and German Shepherds are statistically undifferentiated in their overall rate of narcotics detection accuracy [V(34) = 0.048, p = 0.778] and [χ2(1,34) = 0.080, p = 0.778]. The results further indicate that Belgian Malinois and German Shepherds are statistically undifferentiated in their number of false alerts in narcotics searches [V(34) = 0.133, p = 0.437] and [χ 2(1,34) = 0.604, p = 0.43].

The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures

The spread of SARS-CoV-2, which causes the disease COVID-19, is difficult to control as some positive individuals, capable of transmitting the disease, can be asymptomatic. Thus, it remains critical to generate noninvasive, inexpensive COVID-19 screening systems. Two such methods include detection canines and analytical instrumentation, both of which detect volatile organic compounds associated with SARS-CoV-2. In this study, the performance of trained detection dogs is compared to a noninvasive headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) approach to identifying COVID-19 positive individuals. Five dogs were trained to detect the odor signature associated with COVID-19. They varied in performance, with the two highest-performing dogs averaging 88% sensitivity and 95% specificity over five double-blind tests. The three lowest-performing dogs averaged 46% sensitivity and 87% specificity. The optimized linear discriminant analysis (LDA) model, developed using HS-SPME-GC-MS, displayed a 100% true positive rate and a 100% true negative rate using leave-one-out cross-validation. However, the non-optimized LDA model displayed difficulty in categorizing animal hair-contaminated samples, while animal hair did not impact the dogs' performance. In conclusion, the HS-SPME-GC-MS approach for noninvasive COVID-19 detection more accurately discriminated between COVID-19 positive and COVID-19 negative samples; however, dogs performed better than the computational model when non-ideal samples were presented.

Transforming a Liability into an Asset: A System Dynamics Model for Free-Ranging Dog Population Management

Using Indian free-ranging dogs (FRD) as a case study, we propose a novel intervention of social integration alongside previously proposed methods for dealing with FRD populations. Our study subsumes population dynamics, funding avenues, and innovative strategies to maintain FRD welfare and provide societal benefits. We develop a comprehensive system dynamics model, featuring identifiable parameters customizable for any management context and imperative for successfully planning a widescale FRD population intervention. We examine policy resistance and simulate conventional interventions alongside the proposed social integration effort to compare monetary and social rewards, as well as costs and unintended consequences. For challenging socioeconomic ecological contexts, policy resistance is best overcome by shifting priority strategically between social integration and conventional techniques. The results suggest that social integration can financially support a long-term FRD intervention, while transforming a “pest” population into a resource for animal-assisted health interventions, law enforcement, and conservation efforts.

Development of a Performance Monitoring Instrument for Rating Explosives Search Dog Performance

The growing body of working dog literature includes many examples of scales robustly developed to measure aspects of dog behavior. However, when comparing behavior to working dog ability, most studies rely on training organizations' own long-established ratings of performance, or simply pass/fail at selection or certification as measures of success. Working ability is multifaceted, and it is likely that different aspects of ability are differentially affected by external factors. In order to understand how specific aspects of selection, training, and operations influence a dog's working ability, numerous facets of performance should be considered. An accurate and validated method for quantifying multiple aspects of performance is therefore required. Here, we describe the first stages of formulating a meaningful performance measurement tool for two types of working search dogs. The systematic methodology used was: (1) interviews and workshops with a representative cross-section of stakeholders to produce a shortlist of behaviors integral to current operational performance of vehicle (VS) and high assurance (HAS) search dogs; (2) assessing the reliability and construct validity of the shortlisted behavioral measures (at the behavior and the individual rater level) using ratings of diverse videoed searches by experienced personnel; and (3) selecting the most essential and meaningful behaviors based on their reliability/validity and importance. The resulting performance measurement tool was composed of 12 shortlisted behaviors, most of which proved reliable and valid when assessed by a group of raters. At the individual rater level, however, there was variability between raters in the ability to use and interpret behavioral measures, in particular, more abstract behaviors such as Independence. This illustrates the importance of examining individual rater scores rather than extrapolating from group consensus (as is often done), especially when designing a tool that will ultimately be used by single raters. For ratings to be practically valuable, individual rater reliability needs to be improved, especially for behaviors deemed as essential (e.g., control and confidence). We suggest that the next steps are to investigate why individuals vary in their ratings and to undertake efforts to increase the likelihood that they reach a common conceptualization of each behavioral construct. Plausible approaches are improving the format in which behaviors are presented, e.g., by adding benchmarks and utilizing rater training.

Detecting small and cryptic animals by combining thermography and a wildlife detection dog

Small and cryptic species are challenging to detect and study in their natural habitat. Many of these species are of conservation concern, and conservation efforts may be hampered by the lack of basic information on their ecological needs. Brown hare (Lepus europaeus) leverets - one example of such a small, cryptic and endangered animal - are notoriously difficult to detect, and therefore data on wild leverets are virtually non-existent. Novel technologies and methods such as thermal imaging and the use of wildlife detection dogs represent suitable means for the detection of such species by overcoming the problem of camouflage, using heat or scent emission respectively. Our study on brown hare leverets provides information on how to apply these new techniques successfully for the detection of small and cryptic species, thus enabling the collection of data that was previously inaccessible (e.g. behavioural observation, radio tagging). We found that the choice of method should be made according to vegetative structure. While the handheld thermal imaging camera is best used in areas with no or low vegetative cover, the thermal drone can be used up to medium vegetative cover, whereas the detection dog method is best applied where vegetation is very dense and not suitable to be searched using thermography. Being able to search all sort of different vegetation types, our combined approach enables the collection of a balanced and unbiased dataset regarding habitat type and hence selection of study specimen. We hope that the use of these new techniques will encourage research on many cryptic species that formerly have been neglected because they could not be detected using conventional methodologies.

Performance of detection dogs and visual searches for scat detection and discrimination amongst related species with identical diets

Ecology often faces the problem that many threatened species are highly elusive but also conflict-laden. Thus, proper monitoring data are inevitable for their conservation and management. Indirect monitoring through scats is frequently used for such species, but scats of related species or species with similar diet are often visually indistinguishable. Since genetic methods for species identification are time-consuming and cost-intensive, a verification of the target species beforehand would be extremely beneficial in reducing effort to the analysis of the target species only. Such species discrimination could be provided through species-specific scat detection dogs. Therefore, we evaluated the reliability of species-specific scat detection dogs for two mustelid species feeding on identical diets: the Eurasian otter (Lutra lutra) and the American mink (Neovison vison), both of which are conflict-laden and increasing their populations and distribution ranges in central Europe. Their scats resemble each other in morphology and odour, exacerbating the differentiation even for experts. To evaluate whether detection dogs can reliably discriminate between related species feeding on similar diets and if their use would be beneficial, we tested their abilities against those of humans. We first proved that scat characteristics are not statistically different between species. Likewise, visual species identification through people with different experience levels was only partly successful. Experts showed higher average accuracy (0.89) than non-experts (0.72 and below), but detection dogs (4 dogs) were able to discriminate otter and mink scats under laboratory conditions with an accuracy of 0.95. Moreover, otter scat detection dogs found up to four times more scat samples in the field, were twice as fast as human searchers and found an almost equal number of scats with different characteristics, while humans mostly found older and larger scats placed on hotspots. We conclude that using detection dogs for species identity will allow subsequent laboratory analyses to be species-specific and avoid spending time and money on laboratory work of the wrong species. It also provides more precise and unbiased information about the target species.