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/uk/node/5847102898nas 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