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Dogs are trained for a variety of working roles including assistance, protection, and detection work. Many canine working roles, in their modern iterations, were developed at the turn of the 20th century and training practices have since largely been passed down from trainer to trainer. In parallel, research in psychology has advanced our understanding of animal behavior, and specifically canine learning and cognition, over the last 20 years; however, this field has had little focus or practical impact on working dog training. The aims of this narrative review are to (1) orient the reader to key advances in animal behavior that we view as having important implications for working dog training, (2) highlight where such information is already implemented, and (3) indicate areas for future collaborative research bridging the gap between research and practice. Through a selective review of research on canine learning and behavior and training of working dogs, we hope to combine advances from scientists and practitioners to lead to better, more targeted, and functional research for working dogs.

Detection dogs are widely considered the most effective and adaptive method for explosives detection. Increases in emerging sophisticated threats are accelerating the demand for highly capable explosives detection, causing a strain on available supplies of quality canines worldwide. These strains are further compounded by rigorous behavioral standards required to meet mission-specific capabilities, leading to high rates of dogs disqualified from training or deployment. Ample research has explored the behavioral characteristics important for assistance, guide, and other traditional working roles, while those corresponding to more specialized tasks such as detection of explosives are not as well-understood. In this review we aim to identify the behavioral characteristics important for operational tasks of explosives detection dogs, contrasting with that of other working roles and highlighting key differences between explosives and other types of detection dogs. Further, we review the available research on methods for assessing and selecting candidate detection dogs and make recommendations for future directions and applications to the industry. Improvements and standardization in assessment technology allowing for the identification and enhancement of behavioral characteristics will be key to advancing canine detection technology in general.

The extraordinary olfactory sense of canines combined with the possibility to learn by operant conditioning enables dogs for their use in medical detection in a wide range of applications. Research on the ability of medical detection dogs for the identification of individuals with infectious or non-infectious diseases has been promising, but compared to the well-established and–accepted use of sniffer dogs by the police, army and customs for substances such as money, explosives or drugs, the deployment of medical detection dogs is still in its infancy. There are several factors to be considered for standardisation prior to deployment of canine scent detection dogs. Individual odours in disease consist of different volatile organic molecules that differ in magnitude, volatility and concentration. Olfaction can be influenced by various parameters like genetics, environmental conditions, age, hydration, nutrition, microbiome, conditioning, training, management factors, diseases and pharmaceuticals. This review discusses current knowledge on the function and importance of canines’ olfaction and evaluates its limitations and the potential role of the dog as a biomedical detector for infectious and non-infectious diseases.

Hypogeous fungi (“truffles”) are challenging to study because they produce underground sporocarps that may not be located during traditional fungal surveys. Commercially valuable truffles are located using scent‐detection dogs trained on truffles. However, the dogs are not necessarily limited to commercial truffle species when trained on other taxa of interest. For example, Genea (Pyronemataceae, Ascomycota) is a genus of common but difficult to find truffles that develops small and often soil‐colored hypogeous ascomata. We used a truffle dog trained to locate Genea sporocarps in the southern Willamette Valley and associated mountains in western Oregon, USA, recording when the sporocarps were present at a wide range of elevations (113 to 1879 m). We found Genea was present in half of our surveys and noted that it rarely fruited in areas that had experienced wildfire. This study demonstrates the value of using truffle dogs in documenting truffle diversity, particularly those that are difficult to locate visually, and provides further evidence for the abundance of Genea. Genea is an overlooked but abundant underground fruiting fungus (“truffle”). We used a dog who is trained to find all truffles (not just culinary species) to look for Genea in Oregon. We found that Genea fruits nearly year‐round, but does not often fruit in areas impacted by wildfires.

Background The main strategy to contain the current SARS-CoV-2 pandemic remains to implement a comprehensive testing, tracing and quarantining strategy until vaccination of the population is adequate. Scent dogs could support current testing strategies. Methods Ten dogs were trained for 8 days to detect SARS-CoV-2 infections in beta-propiolactone inactivated saliva samples. The subsequent cognitive transfer performance for the recognition of non-inactivated samples were tested on three different body fluids (saliva, urine, and sweat) in a randomised, double-blind controlled study. Results Dogs were tested on a total of 5242 randomised sample presentations. Dogs detected non-inactivated saliva samples with a diagnostic sensitivity of 84% (95% CI: 62.5–94.44%) and specificity of 95% (95% CI: 93.4–96%). In a subsequent experiment to compare the scent recognition between the three non-inactivated body fluids, diagnostic sensitivity and specificity were 95% (95% CI: 66.67–100%) and 98% (95% CI: 94.87–100%) for urine, 91% (95% CI: 71.43–100%) and 94% (95% CI: 90.91–97.78%) for sweat, 82% (95% CI: 64.29–95.24%), and 96% (95% CI: 94.95–98.9%) for saliva respectively. Conclusions The scent cognitive transfer performance between inactivated and non-inactivated samples as well as between different sample materials indicates that global, specific SARS-CoV-2-associated volatile compounds are released across different body secretions, independently from the patient’s symptoms. All tested body fluids appear to be similarly suited for reliable detection of SARS-CoV-2 infected individuals.

The spotted lanternfly ( Lycorma delicatula , SLF) is an invasive planthopper first detected in the United States in 2014, with initial sightings in Pennsylvania. SLF poses a serious threat to agriculture, particularly targeting grapevines, hops, and ornamental plants, resulting in substantial annual economic losses. Due to its life cycle, the early detection and removal of egg masses are the most effective strategies for preventing long-distance dispersal. However, visual detection by humans is time-consuming and inefficient. Detection dogs have demonstrated high accuracy in locating SLF egg masses and differentiating them from environmental distractors. Despite their effectiveness, the number of dogs available through governmental channels is insufficient to meet demand. This study evaluated whether community scientist dog-handler teams could meet standardized detection criteria using SLF egg masses. Teams from across the U.S. were recruited and trained using devitalized egg masses, with oversight provided by local trainers. Following a 3- to 6-month independent training period, team performance was assessed through an odor recognition test and a field trial. Dogs demonstrated a sensitivity of 82% in controlled testing and 58% in field conditions. These results provide proof of concept; community scientist dog teams could play a significant role in protecting their local environments and agriculture from invasive species.

Introduction: Sniffer dogs can diagnose lung cancer. However, the diagnostic yields of different samples and training methods for lung cancer remain undetermined. Objective: Six dogs were trained in three stages with the aim of improving the diagnostic yield of lung cancer by comparing training methods and specimens. Methods: The pathological tissues of 53 lung cancer patients and 6 non-lung cancer patients in the Department of Thoracic Surgery of Kaohsiung Chang Gung Hospital were collected, and the exhaled breath samples and urine samples were collected. Urine and exhaled breath samples were also collected from 20 healthy individuals. The specimens were sent to the Veterinary Department of Pingtung University of Science and Technology. Results: The dogs had a very low response rate to urine target samples in the first and second stages of training. The experimental results at the second stage of training found that after lung cancer tissue training, dogs were less likely to recognize lung cancer and healthy controls than through breath target training: the response rate to exhaled breathing target samples was about 8–55%; for urine target samples, it was only about 5–30%. When using exhaled air samples for training, the diagnosis rate of these dogs in lung cancer patients was 71.3% to 97.6% (mean 83.9%), while the false positive rate of lung cancer in the healthy group was 0.5% to 27.6% (mean 7.6%). Compared with using breathing target samples for training, the diagnosis rate of dogs trained with lung cancer tissue lung cancer was significantly lower (p < 0.05). The sensitivity and specificity of lung cancer tissue training (50.4% and 50.1%) were lower than the exhaled breath target training (91.7% and 85.1%). There is no difference in lung cancer diagnostic rate by sniff dogs among lung cancer histological types, location, and staging. Conclusion: Training dogs using breathing target samples to train dogs then to recognize exhaled samples had a higher diagnostic rate than training using lung cancer tissue samples or urine samples. Dogs had a very low response rate to urine samples in our study. Six canines were trained on lung cancer tissues and breathing target samples of lung cancer patients, then the diagnostic rate of the recognition of exhaled breath of lung cancer and non-lung cancer patients were compared. When using exhaled air samples for training, the diagnosis rate of these dogs in lung cancer patients was 71.3% to 97.6% (mean 83.9%), while the false positive rate of lung cancer in the healthy group was 0.5% to 27.6% (mean 7.6%). There was a significant difference in the average diagnosis rate of individual dog and overall dogs between the lung cancer group and the healthy group (p < 0.05). When using lung cancer tissue samples for training, lung cancer diagnosis rate of these dogs among lung cancer patients was only 15.5% to 40.9% (mean 27.7%). Compared with using breathing target samples for training, the diagnosis rate of dogs trained with lung cancer tissue lung cancer was significantly lower (p < 0.05). The sensitivity and specificity of lung cancer tissue training (50.4% and 50.1%) were lower than the exhaled breath target training (91.7% and 85.1%). The diagnostic rate of lung cancer by sniffer dogs has nothing to do with the current stage of lung cancer, pathologic type, and the location of tumor mass. Even in stage IA lung cancer, well-trained dogs can have a diagnostic rate of 100%. Using sniffer dogs to screen early lung cancer may have good clinical and economic benefits.

Conservation detection dogs (CDDs) are trained to locate biological material from plants and animals of interest to conservation efforts and are often more effective and economical than other detection methods. However, the financial costs of developing and appropriately caring for CDDs can nonetheless prohibit their use, particularly by smaller conservation organizations. Training skilled volunteers to work with suitable pet dogs may help address this constraint. We sought to further develop the skills of 13 volunteer dog–handler teams that were trained in a previous study to detect myrrh essential oil in controlled laboratory conditions. We assessed search sensitivity, search effort, search precision and false-alert instances through progressive training stages increasing in size and environmental complexity. First, teams searched various-sized areas before and after 12 weeks of search training on a sports-field. Next, teams searched various-sized areas before and after seven weeks of training in bushland. Overall, search sensitivity decreased by approximately 20% in each unfamiliar context, compared to performance in familiar contexts. However, sensitivity typically improved from baseline performance by 10–20% after a period of training. Six teams found at least 78% of targets after training in bushland, yet sensitivity ranged from 29% to 86% between teams. We maintain that the foundational skills developed previously were necessary to prepare volunteer teams for field surveys involving conservation related targets. However, our results highlight the need to also train volunteer CDD teams in search scale and environmental contexts similar to their intended working conditions.

Estimating densities of cougar (Puma concolor) is important for managing cougars and their prey but remains challenging because of cougar's elusive and solitary behavior. To evaluate a non-invasive, genetic capture-recapture method to estimate cougar population size and density, we surveyed a 220-km² area using conservation detection dogs trained to locate scat over a 4-week sampling period in northeast Oregon. We collected 272 scat samples and conducted DNA analysis on 249 samples from which we determined individual identification from 73 samples that represented 21 cougars (9 males and 12 females). We evaluated 4 models to estimate cougar densities: Huggins closed population capture-recapture (Huggins), CAPWIRE, multiple detections with Poisson (MDP), and spatially explicit capture-recapture (SECR). Population estimates for cougars using our study area were 26 (95% CI = 22-35, 9 males and 17 females) from Huggins models, 24 (95% CI = 21-30, 9 males and 15 females) from CAPWIRE, and 27 (95% CI = 24-42, 9 males and 18 females) from the MDP model. We accounted for the edge effect in density estimates caused by individuals whose home ranges included only a portion of the survey grid by buffering the study area using the mean home range radius of 8 cougars equipped with global positioning system collars on or near the study area. We estimated densities of 4.6 cougars/100 km² (95% CI = 3.8-8.3) for the Huggins model, 4.8 cougars/100 km² (95% CI = 4.2-7.8) for the MDP model, 4.2 cougars/100 km² (95% CI = 3.3-5.3) for the CAPWIRE model, and 5.0 cougars/100 km² (95% CI = 3.2-7.7) for the SECR model. Our results suggested estimating cougar densities using scat detection dogs could be feasible at a broader scale with less effort than other methods currently being used.