02678nas a2200253   4500008004100000022001300041245010700054210006900161260001600230300001400246490000700260520191800267653001302185653001602198653001302214653002702227653001502254100001702269700001802286700001802304700001802322700001802340856006602358       2017                        eng d  a1470160X00aTesting the performances of automated identification of bat echolocation calls: A request for prudence0 aTesting the performances of automated identification of bat echo  cJan-07-2017  a416 - 4200 v783 a<p>Echolocating bats are surveyed and studied acoustically with bat detectors routinely and worldwide, yet identification of species from calls often remains ambiguous or impossible due to intraspecific call variation and/or interspecific overlap in call design. To overcome such difficulties and to reduce workload, automated classifiers of echolocation calls have become popular, but their performance has not been tested sufficiently in the field. We examined the absolute performance of two commercially available programs (SonoChiro and Kaleidoscope) and one freeware package (BatClassify). We recorded noise from rain and calls of seven common bat species with Pettersson real-time full spectrum detectors in Sweden. The programs could always (100%) distinguish rain from bat calls, usually (68&ndash;100%) identify bats to group ( Nyctalus/Vespertilio/Eptesicus, Pipistrellus, Myotis, Plecotus, Barbastella ) and usually (83&ndash;99%) recognize typical calls of some species whose echolocation pulses are structurally distinct ( Pipistrellus pygmaeus , Barbastella barbastellus ). Species with less characteristic echolocation calls were not identified reliably, including Vespertilio murinus (16&ndash;26%), Myotis spp. (4&ndash;93%) and Plecotus auritus (0&ndash;89%). All programs showed major although different shortcomings and the often poor performance raising serious concerns about the use of automated classifiers for identification to species level in research and surveys. We highlight the importance of validating output from automated classifiers, and restricting their use to specific situations where identification can be made with high confidence. For comparison we also present the result of a manual identification test on a random subset of the files used to test the programs. It showed a higher classification success but performances were still low for more problematic taxa.</p>
10aBiosonar10amethodology10asoftware10aspecies identification10aultrasound1 aRydell, Jens1 aNyman, Stefan1 aEklöf, Johan1 aJones, Gareth1 aRusso, Danilo  uhttp://linkinghub.elsevier.com/retrieve/pii/S1470160X17301401