02987nas 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/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