02652nas a2200265 4500008004100000245012700041210006900168520174400237653002401981653002302005653001502028653001502043653002302058653002102081100003102102700002502133700002802158700002802186700002802214700003002242700002902272700001902301700002502320856004102345 2022 eng d00aComplementarity between mist-netting and low-cost acoustic recorders to sample bats in Amazonian rainforests and savannahs0 aComplementarity between mistnetting and lowcost acoustic recorde3 a

Effective survey methods are paramount to measure changes in species distribution, populations dynamics and to guide conservation. Mist-netting and passive acoustic monitoring are two of the most used techniques to sample bats assemblages. Yet, despite the great potential of low-cost autonomous ultrasound recorders in surveying bat assemblages, we lack thorough assessments of their performance in relation to more established survey methods. Taking advantage of the rich bat fauna of the northeastern Brazilian Amazon, we set out to i) investigate the complementarity of mist-netting and acoustic surveys in sampling bats in forest and savannah habitats in the Savannahs of Amapá and ii) undertake a cost-effectiveness evaluation of using one, two or three recorders per sampling site to simultaneously survey bat assemblages. The two methods show complementary, and overall species diversity recorded with mist nets was higher than with acoustic recorders. However, species diversity was higher with acoustic recorders than with mist nets when considering a reduced (n < 3) number of transects. In addition, we found a gain in species diversity when using more than one acoustic recorder in forest habitats, despite the low cost-effectiveness. However, there were no differences between the diversity using one, two or three acoustic recorders in savannah. Due to possible device malfunction, we recommend the use of at least two acoustic recorders in both habitats to reduce the likelihood of data loss. The use of low-cost bioacoustic recorders in bat surveys can help to address critical knowledge gaps for poorly known aerial-hawking insectivores and support evidence-based conservation strategies.

10aacoustic monitoring10aAmazonian savannah10aAudiomoths10aChiroptera10aMethods comparison10aNeotropical bats1 aCarvalho, William, Douglas1 aMiguel, João, David1 aXavier, Bruna, da Silva1 aLópez-Baucells, Adrià1 ade Castro, Isaí, Jorge1 aHilário, Renato, Richard1 ade Toledo, José, Júlio1 aRocha, Ricardo1 aPalmeirim, Jorge, M. uhttps://bio.acousti.ca/fr/node/5847102408nas a2200373 4500008004100000245007800041210006900119300001200188490000700200520132500207653002801532653001101560653000901571653002501580653001501605653002501620653001901645653002401664653001401688653001601702653002601718653001901744653001501763653002301778653002101801653002501822653002601847653002101873653002601894653002401920100002701944700002201971856004101993 1999 eng d00aUse of vocal signatures for the inventory of free-flying Neotropical bats0 aUse of vocal signatures for the inventory of freeflying Neotropi a507-5160 v313 a

An ongoing study is being conducted to test the efficacy of the Anabat II detector and analysis system in obtaining reliable vocal signatures for the identification of non-phyllostomid species of bats. We sampled a wide range of elevations and associated habitat types throughout Belize. Anabat provides an instantaneous output of echolocation call structure with a laptop computer. Select sequences can be saved directly to the hard drive, avoiding extraneous noise and sound distortion commonly associated with tape recorders. To date, 18 of the 37 species known or expected
to occur in the study region were identified by recognizable differences in the time-frequency characteristics of echolocation calls. In general, each family is recognizable by call structure patterns and species readily separated by frequency range parameters. Species that commute or forage at high altitudes are not susceptible to capture but are conspicuous by acoustic sampling. Further work is needed to determine limitations of the equipment, establish better sampling procedures, and develop a comprehensive library of vocal signatures incorporating the range of variation
inherent in each species. As this work progresses, we predict the addition of hitherto unknown species occurring within the study region.

10aacoustic identification10aAnabat10abats10aBauerus dubiaquercus10aChiroptera10adiversity assessment10aEmballonuridae10aEptesicus furinalis10ainventory10aMormoopidae10aMormoops megalophylla10aMyotis elegans10aNeotropics10aNoctilio leporinus10aPteronotus davyi10aPteronotus parnellii10aPteronotus personatus10aRhogeessa tumida10aSaccopteryx bilineata10aSaccopteryx leptura1 aO'Farrell, Michael, J.1 aMiller, Bruce, W. uhttps://bio.acousti.ca/fr/node/5805202640nas a2200385 4500008004100000245007700041210006900118300001000187490000700197520153100204653001101735653000901746653001501755653001701770653002401787653001901811653003001830653002201860653001701882653001801899653002201917653002201939653002401961653002301985653002202008653002302030653002602053653002602079653001402105653002102119100002702140700002202167700002402189856004102213 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/fr/node/5805101561nas a2200229 4500008004100000245011800041210006900159300001100228490000600239520086300245653002801108653002401136653000701160653001101167653001301178653001501191653001701206653002301223653002201246100002201268856004101290 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/fr/node/5805002987nas a2200229 4500008004100000245013700041210006900178520218000247653001902427653001702446653001502463653001902478653002602497653003202523100002402555700002802579700002002607700001502627700002002642700002802662856006702690 2020 eng d00aEcological indices in long-term acoustic bat surveys for assessing and monitoring bats' responses to climatic and land-cover changes0 aEcological indices in longterm acoustic bat surveys for assessin3 a

Bats are well known for playing an important role in several ecosystem services such as arthropod population control, insect pest suppression in agricultural systems and vector disease control, but also for acting as ecological indicators. Their population dynamics are strongly linked to environmental variations and, in some cases, reflect the health status of ecosystems. Hence, some species have an excellent potential as ecological indicators due to their sensitivity to ecosystem changes. Despite the general decrease of many bat populations worldwide and the recent upsurge in the use of autonomous acoustic detectors, the acoustic monitoring of bat assemblages is still an emerging field in bat research and conservation. Probably due to a general lack of methodological standards and the lack of common ecological indices, few long-term bat acoustic monitoring programs are currently active and data is rarely shared and compared between regions. In this study we propose and adapt a set of different ecological indices that can be used in acoustic surveys designed to detect changes in bat diversity, activity and assemblage composition, all of which can be linked to species’ climatic and habitat-related preferences. Using a dataset collected during three years of bat monitoring in Catalonia (NE Iberian Peninsula), we used three traditional indices (richness, activity and Shannon diversity) and developed four new ecological indices (Community Thermal Index, Community Precipitation Index, Community Openness Index and Community Specialization Index) that enabled us to study bat communities and compare them at different spatial and temporal scales. Here, we demonstrate the applicability of these indices in bat monitoring programs. We also provide a consistent tool for generating easy-to-interpret ecological indices when monitoring the short- and long-term responses of bats under the current scenario of global change. Using standardized protocols and robust ecological indices enables studies and datasets to be compared, which in turn promotes the development of proper management and conservation measures via international cooperation.

10aBat monitoring10abioacoustics10aChiroptera10aClimate change10aEcological indicators10apassive acoustic monitoring1 aTuneu-Corral, Carme1 aPuig-Montserrat, Xavier1 aFlaquer, Carles1 aMas, Maria1 aBudinski, Ivana1 aLópez-Baucells, Adrià uhttps://linkinghub.elsevier.com/retrieve/pii/S1470160X1930843X02898nas 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/S157495411830023202524nas a2200361 4500008004100000245012900041210006900170520132900239653002801568653001501596653001501611653002301626653001401649653001101663653001501674653001701689653003401706653001801740100002201758700002701780700002501807700003101832700002901863700001701892700003201909700002101941700001901962700002601981700003102007700002002038700001602058856008802074 2019 eng d00aMolossid unlimited: extraordinary extension of range and unusual vocalization patterns of the bat, Promops centralisAbstract0 aMolossid unlimited extraordinary extension of range and unusual 3 a

The big crested mastiff bat, Promops centralis, occurs in Central and South America, but knowledge of its ecology is limited due to its open space hunting strategy, making captures extremely challenging. Notwithstanding, members of the species produce echolocation calls that are easy to identify. After recording calls of P. centralis 1,500 km away from its known range in Brazil, we hypothesized that the distribution range of this species was probably greatly underestimated. To improve the accuracy of P. centralis’ real distribution, we employed acoustic surveys throughout parts of Brazil, conducted after a bibliographic review to gather additional records, and used MaxEnt to model the species’ potential distribution. We have found that P. centralis has a much wider distribution in South America than previously thought, adding more than 3.8 million km2 to its former known area. We also describe an unusual vocalization pattern of P. centralis, with individuals emitting at least three very distinct but highly variable calls. This study shows that bioacoustic surveys and species distribution models can complement traditional methodologies in studying species that are difficult to capture, such as P. centralis, potentially contributing to more effective conservation and management plans.

10abig crested mastiff bat10aChiroptera10aduty cycle10aecholocation calls10afrequency10aMaxEnt10aMolossidae10aSocial calls10aspecies distribution modeling10avocalizations1 aHintze, Frederico1 aArias-Aguilar, Adriana1 aDias-Silva, Leonardo1 aDelgado-Jaramillo, Mariana1 aSilva, Carina, Rodrigues1 aJucá, Thays1 aMischiatti, Francyne, Lyrio1 aAlmeida, Márcio1 aBezerra, Bruna1 aAguiar, Ludmilla, M S1 aPereira, Maria, João Ramo1 aBernard, Enrico1 aLoeb, Susan uhttps://academic.oup.com/jmammal/advance-article/doi/10.1093/jmammal/gyz167/563735603117nas 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