@article {58471, title = {Complementarity between mist-netting and low-cost acoustic recorders to sample bats in Amazonian rainforests and savannahs}, journal = {Community Ecology}, year = {2022}, abstract = {

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 Amap\á 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.

}, keywords = {acoustic monitoring, Amazonian savannah, Audiomoths, Chiroptera, Methods comparison, Neotropical bats}, doi = {10.1007/s42974-022-00131-5}, author = {William Douglas Carvalho and Jo{\~a}o David Miguel and Bruna da Silva Xavier and Adri{\`a} L{\'o}pez-Baucells~ and Isa{\'\i} Jorge de Castro and Renato Richard Hil{\'a}rio and Jos{\'e} J{\'u}lio de Toledo and Ricardo Rocha and Jorge M. Palmeirim} } @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} }