@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 = {<p>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 \&gt;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 (\&gt;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.</p>
},
	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}
}
@article {53289,
	title = {Resource Ephemerality Drives Social Foraging in Bats},
	journal = {Current Biology},
	year = {2018},
	month = {Jan-11-2018},
	abstract = {<p>Observations of animals feeding in aggregations are often interpreted as events of social foraging, but it can be difficult to determine whether the animals arrived at the foraging sites after collective search [1, 2, 3, 4] or whether they found the sites by following a leader [5, 6] or even independently, aggregating as an artifact of food availability [7, 8]. Distinguishing between these explanations is important, because functionally, they might have very different consequences. In the first case, the animals could benefit from the presence of conspecifics, whereas in the second and third, they often suffer from increased competition [3, 9, 10, 11, 12, 13]. Using novel miniature sensors, we recorded GPS tracks and audio of five species of bats, monitoring their movement and interactions with conspecifics, which could be inferred from the audio recordings. We examined the hypothesis that food distribution plays a key role in determining social foraging patterns [14, 15, 16]. Specifically, this hypothesis predicts that searching for an ephemeral resource (whose distribution in time or space is hard to predict) is more likely to favor social foraging [10, 13, 14, 15] than searching for a predictable resource. The movement and social interactions differed between bats foraging on ephemeral versus predictable resources. Ephemeral species changed foraging sites and showed large temporal variation nightly. They aggregated with conspecifics as was supported by playback experiments and computer simulations. In contrast, predictable species were never observed near conspecifics and showed high spatial fidelity to the same foraging sites over multiple nights. Our results suggest that resource (un)predictability influences the costs and benefits of social foraging.</p>
},
	keywords = {bat, behavioral ecology, echolocation, foraging, GPS, movement ecology, navigation, sociobiology},
	issn = {09609822},
	doi = {10.1016/j.cub.2018.09.064},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0960982218312909https://api.elsevier.com/content/article/PII:S0960982218312909?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0960982218312909?httpAccept=text/plain},
	author = {Egert-Berg, Katya and Hurme, Edward R. and Greif, Stefan and Goldstein, Aya and Harten, Lee and Herrera M., Luis Gerardo and Flores-Mart{\'\i}nez, Jos{\'e} Juan and Vald{\'e}s, Andrea T. and Johnston, Dave S. and Eitan, Ofri and Borissov, Ivo and Shipley, Jeremy Ryan and Medellin, Rodrigo A. and Wilkinson, Gerald S. and Goerlitz, Holger R. and Yovel, Yossi}
}
