Abstract
The desert tortoise (Gopherus agassizii) is federally listed as "threatened" and is afforded protection in several U.S. states including California, Nevada, Utah, and Arizona. Numerous factors ranging from habitat destruction to disease are thought to contribute to the species' decline throughout its range. Data collection on desert tortoises in the wild is challenging because tortoises are secretive, and many age and size classes are virtually undetectable in the wild. Detection dogs have been used for decades to assist humans, and the use of dogs for wildlife surveys is of increasing interest to scientists and wildlife managers. To address the basic question of whether dogs could be used to survey for the desert tortoise, we quantified the reliability and efficacy of dogs trained for this purpose. Efficacy is the number of tortoises that dogs find out of a known population. Reliability is a measure of how many times a dog performs its trained alert when it has found a tortoise. A series of experimental trials were designed to statistically quantify these metrics in the field setting where dogs trained to locate live desert tortoises were tested on their ability to find them on the surface, in burrows, and in mark-recapture surveys. Results indicated that dogs are effective at and can safely locate desert tortoises with reliability on the surface and are capable of detecting tortoises in burrows under a range of environmental conditions. Dogs found tortoises at the same statistical rate at temperatures between 12 degrees and 27 degrees C, relative humidity from 16% to 87%, and wind speeds up to 8 m/s. In both surface and burrow trials, dogs found >90% of the experimental animals. In comparative studies with humans, dogs found tortoises as small as 30 mm, whereas the smallest tortoise located by human survey teams was 110 mm. Although not all dogs or dog teams meet the requirements to conduct wildlife surveys, results from this study show the promise in using dogs to increase our knowledge of rare, threatened, and endangered species through improved data collection methods.
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