02087nas a2200325 4500008004100000245005400041210004600095260001600141300001400157490000700171520124800178653001301426653001301439653001001452653002201462653001801484653001201502653000901514653001501523653003101538653001201569653001601581100002101597700002001618700002301638700002001661700001601681700002101697856004301718 2018 eng d00aClicks of dwarf sperm whales ( Kogia sima)0 aClicks of dwarf sperm whales iKogia simai cJan-10-2018 a963 - 9780 v343 a

The two species of the genus Kogia are widely distributed throughout the world's temperate and tropical oceans, but because they are small and highly cryptic, they are difficult to monitor. The acoustic signals of K. breviceps have been described previously, but the signals of K. sima have remained unknown. Here we present three recordings of K. sima, two from free‐ranging animals and one from a captive setting, representing both the Atlantic Ocean and Pacific Ocean. The acoustic signals of K. sima are very similar to the signals of K. breviceps and other species that have narrow‐band, high‐frequency (NBHF) clicks. Free‐ranging K. sima produce “usual” clicks that have mean peak and centroid frequencies of 127–129 kHz, mean −3 dB bandwidth of 10 kHz, mean −10 dB bandwidth of 16–17 kHz, and mean interclick interval of 110–164 ms. Although K. sima clicks cannot yet be distinguished from those of K. breviceps or other NBHF clicking species, our detailed description of this species' signals reveals the similarities between the two Kogia species, and thus allows for passive acoustic monitoring of the genus Kogia in regions where other NBHF species are not present.

10aAtlantic10aBiosonar10aclick10adwarf sperm whale10aecho-location10aFlorida10aGuam10aKogia sima10anarrow-band high-frequency10aPacific10aThe Bahamas1 aMerkens, Karlina1 aMann, David, A.1 aJanik, Vincent, M.1 aClaridge, Diane1 aHill, Marie1 aOleson, Erin, M. uhttp://doi.wiley.com/10.1111/mms.v34.402678nas 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

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 (68–100%) identify bats to group ( Nyctalus/Vespertilio/Eptesicus, Pipistrellus, Myotis, Plecotus, Barbastella ) and usually (83–99%) 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 (16–26%), Myotis spp. (4–93%) and Plecotus auritus (0–89%). 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.

10aBiosonar10amethodology10asoftware10aspecies identification10aultrasound1 aRydell, Jens1 aNyman, Stefan1 aEklöf, Johan1 aJones, Gareth1 aRusso, Danilo uhttp://linkinghub.elsevier.com/retrieve/pii/S1470160X17301401