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.

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 relational database was created in Microsoft Access 2013. It contains five basic tables (literature, dog breeds, target species, target types and countries) and one main table (Study). The basic tables and the main table are connected through unique identifier (IDs). Any potential query can be built with this structure.

We included a few pre-defined queries:

Query_Breed_Human_comparison – A summary about which breed was better or not than any other method for which species
Query_Number_of_dogs_breed – A list of how many dogs have been used per breed
Query_Target_Species_Publications – A list of all target species and the publications mentioning them
Query_Type_of_Source_Continent – A list of all publications by continent separated by type of source
Query_Type_of_Source_Year – A chronological list of all publications separated by type of source
Query_Year_Continent – A chronological list of all publications separated by continent

Note that double-mentioning is possible, e.g. when publications mention several continents.

The main query is called Target Species Query. It contains all 2465 single cases (based on the main table, Study) and all information connected to it.

We encourage usage or extensions of the database but kindly ask to cite our original data properly.