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      Training of a dog for the monitoring of Osmoderma eremita

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          Abstract

          One aim of the MIPP Project (http://www.lifemipp.eu) was to develop non-invasive monitoring methods for selected saproxylic beetles. In this paper, a method is proposed for monitoring the larvae of Osmoderma eremita in their natural habitat (i.e. hollow trees), using a conservation detection dog (CDD). Wood mould sampling (WMS), the standard method to detect hermit beetles and other saproxylic insects inside tree hollows, is time-consuming and exposes the target species and the whole saproxylic communities to some risks. In contrast, CDDs pose no risk to the species living inside trees while, at the same time, offer a powerful tool for surveying the insects. In this paper, the methods applied to train the dog are presented, together with the results for accuracy (the overall proportion of correct indications), sensitivity (the proportion of correct positive indications) and specificity (the proportion of correct negative indications) obtained once the CDD had been fully trained. Results are presented for nitrocellulose filters with the odour of the target species, for larvae living inside hollow trees, for frass and for the remains of adults. A comparison of the efficiency between CDD and WMS showed that employing the dog was much less time-consuming than WMS. The literature on training CDDs for nature conservation tasks, with particular reference to cases involving Coleoptera, was also reviewed.

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            Effectiveness of Scat Detection Dogs for Detecting Forest Carnivores

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              A Comparison of Survey Methods for Detecting Bobcats

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                Author and article information

                Journal
                Nature Conservation
                NC
                Pensoft Publishers
                1314-3301
                1314-6947
                August 28 2017
                August 28 2017
                : 20
                : 237-264
                Article
                10.3897/natureconservation.20.12688
                © 2017

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