@article {58051, title = {Qualitative Identification of Free-flying Bats Using the Anabat Detector}, journal = {Journal of MammalogyJournal of Mammalogy}, volume = {80}, number = {1}, year = {1999}, note = {JRS}, pages = {11-23}, abstract = {

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.

}, keywords = {Anabat, bats, Chiroptera, echolocation, Eptesicus furinalis, identification, Lasionycteris noctivagans, Lasiurus cinereus, Lasiurus ega, Molossus ater, Molossus molossus, Molossus sinaloae, Myotis californicus, Myotis ciliolabrum, Myotis yumanensis, Noctilio leporinus, Saccopteryx bilineata, Tadarida brasiliensis, technique, vocal signatures}, author = {O{\textquoteright}Farrell, Michael J. and Miller, Bruce W. and Gannon, William L.} } @article {58050, title = {A method for determining relative activity of free flying bats using a new activity index for acoustic monitoring}, journal = {Acta ChiropterologicaActa Chiropterologica}, volume = {3}, number = {1}, year = {2001}, pages = {93-105}, abstract = {

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.

}, keywords = {acoustic activity index, acoustic monitoring, AI, Anabat, bat pass, Chiroptera, echolocation, relative abundance, relative activity}, author = {Miller, Bruce W.} } @article {57929, title = {Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys}, year = {2019}, abstract = {

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.

}, keywords = {Amazon, bioacoustics, Chiroptera, echolocation, Machine-learning algorithms}, doi = {10.1016/j.ecoinf.2018.11.004}, url = {https://linkinghub.elsevier.com/retrieve/pii/S1574954118300232}, author = {L{\'o}pez-Baucells, Adri{\`a} and Torrent, Laura and Rocha, Ricardo and Paulo E.D. Bobrowiec and Jorge M. Palmeirim and Christoph F.J. Meyer} } @article {53351, title = {Stronger together: Combining automated classifiers with manual post-validation optimizes the workload vs reliability trade-off of species identification in bat acoustic surveys}, journal = {Ecological Informatics}, year = {2018}, month = {Jan-11-2018}, abstract = {

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.

}, keywords = {Amazon, bioacoustics, Chiroptera, echolocation, Machine-learning algorithms}, issn = {15749541}, doi = {10.1016/j.ecoinf.2018.11.004}, url = {https://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}, author = {L{\'o}pez-Baucells, Adri{\`a} and Torrent, Laura and Rocha, Ricardo and Paulo E.D. Bobrowiec and Jorge M. Palmeirim and Christoph F.J. Meyer} }