02643nas a2200385 4500008004100000245007700041210006900118300001000187490000700197520153100204653001101735653000901746653001501755653001701770653002401787653001901811653003001830653002201860653001701882653001801899653002201917653002201939653002401961653002301985653002202008653002302030653002602053653002602079653001402105653002102119100002702140700002202167700002402189856004402213 1999 eng d00aQualitative Identification of Free-flying Bats Using the Anabat Detector0 aQualitative Identification of Freeflying Bats Using the Anabat D a11-230 v803 a

A variety of ultrasonic bat detectors have been used over the past 3 decades to identify freeflying bats. Analyses of recorded echolocation calls were slow and typically restricted to few calls and at a resolution obscuring details of call structure. The Anabat II detector and associated zerocrossings analysis system allows an immediate examination, via a laptop computer, of the timefrequency structure of calls as they are detected. These calls can be stored on the hard drive for later examination, editing, and measurement. Many North American bats can be identified to species by qualitatively using certain structural characteristics of calls, primarily approximate maximum and minimum frequencies and morphological aspects of calls e.g., linearity and changes in slope). To identify calls precisely, it is important to use a continuous sequence of calls from an individual in normal flight rather than from single isolated calls. All calls are not equally useful, and many fragmentary calls must be discarded before making a determination. Each sequence of calls must be examined to ensure that multiple bats have not been simultaneously recorded, which confounds correct identification. We found the percentage of nonusable calls within usable vocal sequences to be highest in vespertilionids 20-40%), whereas for other families this was frequently <10%. Active rather than passive collection of data maximizes quality and quantity of diagnostic calls and provides a contextual base for the investigator.

10aAnabat10abats10aChiroptera10aecholocation10aEptesicus furinalis10aidentification10aLasionycteris noctivagans10aLasiurus cinereus10aLasiurus ega10aMolossus ater10aMolossus molossus10aMolossus sinaloae10aMyotis californicus10aMyotis ciliolabrum10aMyotis yumanensis10aNoctilio leporinus10aSaccopteryx bilineata10aTadarida brasiliensis10atechnique10avocal signatures1 aO'Farrell, Michael, J.1 aMiller, Bruce, W.1 aGannon, William, L. uhttps://bio.acousti.ca/pt-pt/node/5805101564nas a2200229 4500008004100000245011800041210006900159300001100228490000600239520086300245653002801108653002401136653000701160653001101167653001301178653001501191653001701206653002301223653002201246100002201268856004401290 2001 eng d00aA method for determining relative activity of free flying bats using a new activity index for acoustic monitoring0 amethod for determining relative activity of free flying bats usi a93-1050 v33 a

A new activity index for acoustical bat data is presented. The AI acoustical activity index) was highly correlated to bat passes but proved to be a less biased index of activity. The method dispenses with the need to define, identify and count bat passes and provides a simple means to quantify activity. This method uses the Anabat system where acoustic surveys are carried out in real time with the data saved directly to a computer hard drive, taking advantage of the date-time information encoded into each file. The method is based upon the presence absence of a species occurrence during one-minute time intervals and avoids inflating an index of activity that may reflect the behavior of the species sampled. Examples are given showing that the AI is an effective measure of bat activity allowing comparisons between sites, times and species.

10aacoustic activity index10aacoustic monitoring10aAI10aAnabat10abat pass10aChiroptera10aecholocation10arelative abundance10arelative activity1 aMiller, Bruce, W. uhttps://bio.acousti.ca/pt-pt/node/5805002898nas a2200217 4500008004100000245018100041210006900222520208500291653001102376653001702387653001502404653001702419653003202436100002802468700001902496700001902515700002702534700002502561700002702586856006702613 2019 eng d00aStronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys0 aStronger together Combining automated classifiers with manual po3 a

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of >650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (>90%). Adopting a desired accuracy probability threshold of 95% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.

10aAmazon10abioacoustics10aChiroptera10aecholocation10aMachine-learning algorithms1 aLópez-Baucells, Adrià1 aTorrent, Laura1 aRocha, Ricardo1 aBobrowiec, Paulo, E.D.1 aPalmeirim, Jorge, M.1 aMeyer, Christoph, F.J. uhttps://linkinghub.elsevier.com/retrieve/pii/S157495411830023203117nas a2200241 4500008004100000022001300041245018100054210006900235260001600304520208500320653001102405653001702416653001502433653001702448653003202465100002802497700001902525700001902544700002702563700002502590700002702615856023302642 2018 eng d a1574954100aStronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys0 aStronger together Combining automated classifiers with manual po cJan-11-20183 a

Owing to major technological advances, bioacoustics has become a burgeoning field in ecological research worldwide. Autonomous passive acoustic recorders are becoming widely used to monitor aerial insectivorous bats, and automatic classifiers have emerged to aid researchers in the daunting task of analysing the resulting massive acoustic datasets. However, the scarcity of comprehensive reference call libraries still hampers their wider application in highly diverse tropical assemblages. Capitalizing on a unique acoustic dataset of >650,000 bat call sequences collected over a 3-year period in the Brazilian Amazon, the aims of this study were (a) to assess how pre-identified recordings of free-flying and hand-released bats could be used to train an automatic classification algorithm (random forest), and (b) to optimize acoustic analysis protocols by combining automatic classification with visual post-validation, whereby we evaluated the proportion of sound files to be post-validated for different thresholds of classification accuracy. Classifiers were trained at species or sonotype (group of species with similar calls) level. Random forest models confirmed the reliability of using calls of both free-flying and hand-released bats to train custom-built automatic classifiers. To achieve a general classification accuracy of ~85%, random forest had to be trained with at least 500 pulses per species/sonotype. For seven out of 20 sonotypes, the most abundant in our dataset, we obtained high classification accuracy (>90%). Adopting a desired accuracy probability threshold of 95% for the random forest classifier, we found that the percentage of sound files required for manual post-validation could be reduced by up to 75%, a significant saving in terms of workload. Combining automatic classification with manual ID through fully customizable classifiers implemented in open-source software as demonstrated here shows great potential to help overcome the acknowledged risks and biases associated with the sole reliance on automatic classification.

10aAmazon10abioacoustics10aChiroptera10aecholocation10aMachine-learning algorithms1 aLópez-Baucells, Adrià1 aTorrent, Laura1 aRocha, Ricardo1 aBobrowiec, Paulo, E.D.1 aPalmeirim, Jorge, M.1 aMeyer, Christoph, F.J. uhttps://linkinghub.elsevier.com/retrieve/pii/S1574954118300232https://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1574954118300232?httpAccept=text/plain