DOI: 10.5061/dryad.t76hdr804
title: Detection dogs in nature conservation: a database on their worldwide deployment with a review on breeds used and their performance compared to other methods

description: 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.
description: 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.
description: This relational database was created in Microsoft Access 2013. It
 contains five basic tables (<em>literature, dog breeds, target
 species, target types </em>and <em>countries</em>) and
 one main table (<em>Study</em>). 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:
 <em>Query_Breed_Human_comparison</em> – A summary
 about which breed was better or not than any other method for which
 species<br> <em>Query_Number_of_dogs_breed</em> – A list
 of how many dogs have been used per breed<br>
 <em>Query_Target_Species_Publications</em> – A list of all
 target species and the publications mentioning them<br>
 <em>Query_Type_of_Source_Continent</em> – A list of all
 publications by continent separated by type of source<br>
 <em>Query_Type_of_Source_Year</em> – A chronological list of
 all publications separated by type of source<br>
 <em>Query_Year_Continent</em> – 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 <em>Target
 Species Query</em>. It contains all 2465 single cases (based on the
 main table, <em>Study</em>) and all information connected to
 it. We encourage usage or extensions of the database
 but kindly ask to cite our original data properly.