Over the last century, dogs have been increasingly used to detect rare and elusive species or traces of them. The use of wildlife detection dogs (WDD) is particularly well established in North America, Europe and Oceania, and projects deploying them have increased worldwide. However, if they are to make a significant contribution to conservation and management, their strengths, abilities, and limitations should be fully identified. We reviewed the use of WDD with particular focus on the breeds used in different countries and for various targets, as well as their overall performance compared to other methods, by developing and analysing a database of 1220 publications, including 916 scientific ones, covering 2464 individual cases - most of them (1840) scientific. With the worldwide increase in the use of WDD, associated tasks have changed and become much more diverse. Since 1930, reports exist for 62 countries and 407 animal, 42 plant, 26 fungi and 6 bacteria species. Altogether, 108 FCI-classified and 20 non-FCI-classified breeds have worked as WDD. While certain breeds have been preferred on different continents and for specific tasks and targets, they were not generally better suited for detection tasks than others. Overall, WDD usually worked more effectively than other monitoring methods. For each species group, regardless of breed, detection dogs were better than other methods in 88.71% of all cases and only worse in 0.98%. It was only for arthropods that Pinshers and Schnauzers performed worse than other breeds. For mono- and dicotyledons, detection dogs did less often outperform other methods. Although every breed can be trained as a WDD, choosing the most suitable dog for the task and target may speed up training and increase the chance of success. Albeit selection of the most appropriate WDD is important, excellent training, knowledge about the target density and suitability, and a proper study design all appeared to have the highest impact on performance. Moreover, an appropriate area, habitat and weather are crucial for detection dog work. When these factors are taken into consideration, WDD can be an outstanding monitoring method.

Methods We systematically searched for any publication using the following search terms in Google Scholar and ISI Web of Knowledge: wildlife detect* dog, species detect* dog, scat detect* dog, [species] + detect* dog, [author] + detect* dog, [country] + detect* dog, conservation (detect*) dog, predator (detect*) dog, protected species (detect*) dog, den detect* dog, roost detect* dog, plant detect* dog, canine detection, and tracking dog. We traced any potentially relevant cited publication and only included those in our review that we could check ourselves. We also collected publications if we got to know them otherwise and reviewed existing literature lists and compilations (Grimm-Seyfarth et al. 2021, Appendix S1.1). We focused mainly on scientific literature, including scientific papers, dissertations, and project reports. However, wildlife detection dogs were frequently used for conservation or management purposes without a scientific research project behind them. For a more comprehensive overview of their deployment and performance, we included popular science or newspaper articles when no scientific publication about the project was found. In addition, we used social media platforms to obtain many articles from different countries (Grimm-Seyfarth et al. 2021, Appendix S1.1). In order to avoid multiple citations of the same study for which publications from different sources have been published, we compared each new entry with the entries in the database and preferably included scientific publications, followed by books, popular science and newspaper articles.

We compiled the data in a relational database (Microsoft Access 2013) consisting of five basic tables: literature, dog breeds, target species, target types and countries. We classified dog breeds into the ten FCI classification groups and breeds not listed as “not classified”. We assigned mixed breeds to a main or first-mentioned breed or to the category “Mix” when they could not be assigned to a specific breed. We classified target species according to their Latin and English names, genus, family, order, class, phylum and kingdom, adding subspecies names if provided. If the dog detected species groups without further specification (e.g., bat or bird carcasses, rodents, weed), we retained this group only. Taxonomic changes due to splitting of taxa into several species were only made if the allocation to the new species was obvious from the geographic information provided or had already been done by other authors. We divided potential target types into: living or dead individuals; nests, dens, clutches, coveys, roosts; scat, urine, saliva, glandular secretion; spores, eggs; larvae; hair, feathers, pellets, shed skin; and different combinations thereof. Lastly, we classified countries according to the (sub-) continent into North, Central and South America, Europe, Asia, Africa, and Oceania, assigning Russia and Turkey to “Eurasia”. Furthermore, we assigned Australia, New Zealand, and all oceanic islands (including subantarctic islands) to “Oceania” and made no differentiation to Zealandia.

In a main table, we then assigned each breed-target species-country association per reference as a single “case”. We marked pure-breed dogs and added a second breed for mixed breeds (if provided), as well as the number of dogs per breed and reference (if not mentioned directly, “1” for mentioning “dog” and “2” for mentioning “dogs”). We also added specifications to the country (e.g. Islands). If available, we extracted results of the wildlife detection dog performance compared to other monitoring methods. We classified the performance into four categories: dogs were (i) better; (ii) equal; or (iii) worse than other methods tested; or (iv) mixed results. The factor in comparison was study-specific and could include speed per area or transect, area size, sample size, quality, detectability, specificity, sensitivity, accuracy, or precision. We relied on those conservative measures since different monitoring methods can hardly be compared otherwise. The category “mixed results” was given when the dogs were better at some factors but worse at others, or when the performance depended upon season, year, site, or dog. Since we designed the database as a relational database, IDs among the five basic tables and the main table were linked together for quick searches and queries.

This data tracks the spatial extent of black-tailed prairie dog colonies on OSMP-managed lands over time, including any held in fee and on conservation easements where the department has a management agreement in place.Data was collected using GPS and clipped to the City of Boulder Open Space and Mountain Parks (OSMP) and Boulder County Parks and Open Space (BCPOS) properties. It has been collected yearly each fall since 1996, by OSMP wildlife staff. Black-tailed prairie dog colonies create a unique habitat on the landscape. They create habitat and food for other animals of federal, state, and local conservation concern (e.g. burrowing owls, ferruginous hawks, bald and golden eagles, American badger, etc., (see the OSMP Grassland Ecosystem Management Plan for more details)). Their burrowing activity also causes conflicts when it occurs on parcels where the management focus is on agriculture or other purposes. The conflict can be especially high in areas of irrigated grasslands since the burrowing activity can alter how water is applied to the landscape, and prairie dog browsing can remove graminoid cover and encourage invasions of tenacious non-native form species. System-wide mapping was first initiated by the mandate to monitor black-tailed prairie dogs in the “City of Boulder Grassland Management: Black-tailed Prairie Dog Habitat Conservation Plan”. This plan was approved by the City of Boulder Open Space Board of Trustees on March 13, 1996. Annual system-wide mapping began that fall, and continued each subsequent fall starting on Sept 1. In 2012 a field was added to distinguish active vs inactive colonies. At this time we began also collecting inactive colony boundaries.The spatial data informs the public, lessees, academic researchers, and partnering agencies as to the extent of the black-tailed prairie dogs on our properties. This data informs conservation planning for sensitive species, including the federally endangered black-footed ferret. The annual mapping can be used to visually demonstrate how populations fluctuate, highlight areas of conflict, and inform management decisions. This long term data set allows for a retrospective view of where prairie dogs have occurred on the system in the past, but where they may no longer persist. This historic view helps staff identify areas where prairie dogs are likely to become reestablished, either through natural recolonization or by direct relocation. Information on where prairie dogs have or do exist also helps inform Habitat Suitability Models. The data set also provides staff with tools to make management decision based on colony management designations (Prairie Dog Conservation Area, Grassland Preserve, Multiple Objective Area, Transition Area, Removal Area (see OSMP Grassland Ecosystem Management Plan for specifics on the designation process)) The data is not meant to estimate the population of individual animals on the system or to estimate colony density.

Dog runs in New York City Department of Parks & Recreation properties and properties with off-leash hours for dogs.

PetSmart Inc., the American retail chain, accounted for almost a third of the pet store market, based on revenue in 2023. The pet store market is highly concentrated in the United States, with the two leading players, PetSmart and PETCO Animal Supplies, accounting for almost 40 percent of the total market revenue that year.

PetSmart origins

PetSmart, originally named PetFood Warehouse, was founded in 1986 when two stores were opened in Phoenix, Arizona. The company continued to grow and went public in 1993. In the 2021/22 fiscal year, PetSmart’s revenue reached close to 6.7 billion U.S. dollars.

Online pet retail

With the growth of the e-commerce market, came greater online sales numbers, a shift that is also visible in the household and pet care market. In 2020, around a fifth of all household and pet care sales worldwide were made online, which is double the share seen five years earlier. By 2025, nearly a third of this category’s sales are projected to be e-commerce sales. To buy pet products specifically, the most common e-commerce websites used by U.S. consumers were Amazon.com, Walmart.com, and Chewy.com.

This data set contains the freewheeling possibilities for tested dogs only on paths, paths and lawns in public green and recreational facilities in accordance with § 9(3) of the Hamburg Dogs Act in the Hanseatic City of Hamburg. This freewheeling option exists for dogs that have successfully passed the obedience test and are therefore exempt from the general leashing obligation. After the exemption, the dogs may be carried unchallenged wherever there are no ‘special’ leashing obligations and no travel bans. In addition to roads, paths and traffic areas - which are automatically approved for these dogs - these are also certain paths, paths and lawns in public green and recreational areas. The district offices have designated these areas so that they can be used as a further offer by dog owners.

ObjectiveThe purpose of this study was to compare previously identified demographic risk factors for injury in agility dogs, and explore other potential associations with demographic risk factors in new populations, and across different levels of injury severity.ProceduresAn internet-based survey of agility handlers was conducted. The primary outcome was if the dog had ever had an injury that kept from agility for over a week. Demographic information about the dog and handler were recorded. Logistic regression was used to quantify associations between variables of interest with injury history and all models were adjusted for age. Analyses were stratified by geographic location. Final model building was done via backward selection.ResultsThe sample included 2,962 dogs from North America and 1,235 dogs from elsewhere. In the North American sample, 8 variables were associated with injury history; dog breed, height and weight, handler age, gender, agility experience, competing at the national level, age dog was acquired, and taking radiographs to assess growth plate closure. In the non-North American sample, 4 variables were associated with injury history; breed, handler age, occupation (dog trainer or not), and handler medical training. In both samples, Border Collies showed a marked increase in injury risk (ORs 1.89 and 2.34) and handler age >65 was associated with lower risk (ORs 0.62 and 0.77). Consistent with previous studies, greater handler experience was associated with reduced risk in the North American sample, but the other sample did not show this pattern, even in unadjusted models. Dog spay/neuter status was not associated with injury risk in either sample.Conclusions and Clinical RelevanceDogs with radiographs assessing growth plate closure may have increased injury risk as this population of owners may plan to train their dog harder, and at an earlier age. This finding also poses the question of whether or not growth plate closure is a good indicator of safety for increasing training intensity. Knowledge of what risk factors exist for injury in agility dogs is imperative in determining direction for future prospective studies, as well as creating recommendations to help prevent injury in this population of dogs.

List of dogs registered in Adelaide City Council area (Adelaide and North Adelaide) for a particular period. Information provided includes dog name, breed, period, gender, current status, class, transaction type and suburb. Note: Normal – means one dog registered to the property. Normal multiple – means there is more than one dog registered (2 or more dogs).

Control of dog-mediated rabies relies on raising awareness, access to post-exposure prophylaxis (PEP) and mass dog vaccination. To assess rabies awareness in Moramanga district, Madagascar, where rabies is endemic, two complementary quantitative and qualitative approaches were carried out in 2018. In the quantitative approach, a standardized questionnaire was administered to 334 randomized participants living in 170 households located less than 5 km from the anti-rabies treatment center (ARTC) located in Moramanga city (thereafter called the central area), and in 164 households located more than 15 km away from the ARTC in two rural communes (thereafter called the remote area). Logistic regression models were fitted to identify factors influencing knowledge and practice scores. The qualitative approach consisted in semi-structured interviews conducted with 28 bite victims who had consulted the ARTC, three owners of biting dogs, three ARTC staff and two local authorities.Overall, 15.6% (52/334) of households owned at least one dog. The dog-to-human ratio was 1:17.6. The central area had a significantly higher dog bite incidence (0.53 per 100 person-years, 95% CI: 0.31–0.85) compared to the remote area (0.22 per 100 person-years, 95% CI: 0.09–0.43) (p = 0.03). The care pathway following a bite depended on wound severity, how the dog was perceived and its owner’s willingness to cover costs. Rabies vaccination coverage in dogs in the remote area was extremely low (2.4%). Respondents knew that vaccination prevented animal rabies but owners considered that their own dogs were harmless and cited access and cost of vaccine as main barriers. Most respondents were not aware of the existence of the ARTC (85.3%), did not know the importance of timely access to PEP (92.2%) or that biting dogs should be isolated (89.5%) and monitored. Good knowledge scores were significantly associated with having a higher socio-economic status (OR = 2.08, CI = 1.33–3.26) and living in central area (OR = 1.91, CI = 1.22–3.00). Good practice scores were significantly associated with living in central area (OR = 4.78, CI = 2.98–7.77) and being aware of the ARTC’s existence (OR = 2.29, CI = 1.14–4.80).In Madagascar, knowledge on rabies was disparate with important gaps on PEP and animal management. Awareness campaigns should inform communities (i) on the importance of seeking PEP as soon as possible after an exposure, whatever the severity of the wound and the type of biting dog who caused it, and (ii) on the existence and location of ARTCs where free-of-charge PEP is available. They should also encourage owners to isolate and monitor the health of biting dogs. Above all, awareness and dog vaccination campaigns should be designed so as to reach the more vulnerable remote rural populations as knowledge, good practices and vaccination coverage were lower in these areas. They should also target households with a lower socio-economic status. If awareness campaigns are likely to succeed in improving access to ARTCs in Madagascar, their impact on prompting dog owners to vaccinate their own dogs seems more uncertain given the financial and access barriers. Therefore, to reach the 70% dog vaccination coverage goal targeted in rabies elimination programs, awareness campaigns must be combined with free-of-charge mass dog vaccination.