02202nas a2200157 4500008004100000245009000041210006900131260001600200520166600216100003201882700002501914700001801939700002501957700002101982856004102003 2018 eng d00aCatching insects while recording bats: impacts of light trapping on acoustic sampling0 aCatching insects while recording bats impacts of light trapping  cApr-01-20203 a

Collecting information on bat prey availability usually involves the use of light traps to capture moths and flies that constitute the main prey items of most insectivorous bats. However, despite the recent awareness on the adverse effects of light on bats, little is known regarding the potential impacts of light trapping on the bat sampling outcomes when passive acoustic sampling and light trap- ping are implemented simultaneously. Using a before–after experimental design that involved the installation of a 6 W actinic light trap 1 m away from the bat detector, we tested the predictions that (1) slow-flying bat species will be less active when the light trap is present, while the opposite will be true for fast-fly- ing species; and (2) bat species richness will be lower at lit conditions compared to dark ones. Our results suggest that the use of light traps in combination with bat detectors may considerably influence the outcomes of acoustic sampling. Although the activity of fast-flying bat species did not differ between the two treatments, we found that the activity of slow-flying ones such as Rhinolophus ferrumequinum and Rhinolophus hipposideros decreased significantly at lit condi- tions. Furthermore, we recorded fewer bat species when the light trap was deployed. To overcome this issue, we strongly recommend either (1) placing light traps at a considerable distance from bat detectors; or (2) using light traps during the night that follows the bat sampling if sampling needs to be at the same position; or (3) deploying non-attractant insect traps such as Malaise traps if Lepidoptera is not the main order targeted.

1 aFroidevaux, Jérémy, S. P.1 aFialas, Penelope, C.1 aJones, Gareth1 aPettorelli, Nathalie1 aMerchant, Nathan uhttp://doi.wiley.com/10.1002/rse2.7101828nas a2200181 4500008004100000022001300041245007600054210006900130260001600199300001600215490000700231520124800238100002501486700002901511700002201540700001801562856006601580 2010 eng d a0960982200aAn Aerial-Hawking Bat Uses Stealth Echolocation to Counter Moth Hearing0 aAerialHawking Bat Uses Stealth Echolocation to Counter Moth Hear cJan-09-2010 a1568 - 15720 v203 a

Ears evolved in many nocturnal insects, including some moths, to detect bat echolocation calls and evade capture [1, 2]. Although there is evidence that some bats emit echolocation calls that are inconspicuous to eared moths, it is difficult to determine whether this was an adaptation to moth hearing or originally evolved for a different purpose [2, 3]. Aerial-hawking bats generally emit high-amplitude echolocation calls to maximize detection range [4, 5]. Here we present the first example of an echolocation counterstrategy to overcome prey hearing at the cost of reduced detection distance. We combined comparative bat flight-path tracking and moth neurophysiology with fecal DNA analysis to show that the barbastelle, Barbastella barbastellus, emits calls that are 10 to 100 times lower in amplitude than those of other aerial-hawking bats, remains undetected by moths until close, and captures mainly eared moths. Model calculations demonstrate that only bats emitting such low-amplitude calls hear moth echoes before their calls are conspicuous to moths. This stealth echolocation allows the barbastelle to exploit food resources that are difficult to catch for other aerial-hawking bats emitting calls of greater amplitude.

1 aGoerlitz, Holger, R.1 aHofstede, Hannah, M. ter1 aZeale, Matt, R.K.1 aJones, Gareth uhttp://linkinghub.elsevier.com/retrieve/pii/S096098221000991702678nas 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