Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat

BackgroundMultiple methods have been developed to infer behavioral states from animal movement data, but rarely has their accuracy been assessed from independent evidence, especially for location data sampled with high temporal resolution. Here we evaluate the performance of behavioral segmentation methods using acoustic recordings that monitor prey capture attempts.MethodsWe recorded GPS locations and ultrasonic audio during the foraging trips of 11 Mexican fish-eating bats, Myotis vivesi, using miniature bio-loggers. We then applied five different segmentation algorithms (k-means clustering, expectation-maximization and binary clustering, first-passage time, hidden Markov models, and correlated velocity change point analysis) to infer two behavioral states, foraging and commuting, from the GPS data. To evaluate the inference, we independently identified characteristic patterns of biosonar calls (“feeding buzzes”) that occur during foraging in the audio recordings. We then compared segmentation methods on how well they correctly identified the two behaviors and if their estimates of foraging movement parameters matched those for locations with buzzes.ResultsWhile the five methods differed in the median percentage of buzzes occurring during predicted foraging events, or true positive rate (44–75%), a two-state hidden Markov model had the highest median balanced accuracy (67%). Hidden Markov models and first-passage time predicted foraging flight speeds and turn angles similar to those measured at locations with feeding buzzes and did not differ in the number or duration of predicted foraging events.ConclusionThe hidden Markov model method performed best at identifying fish-eating bat foraging segments; however, first-passage time was not significantly different and gave similar parameter estimates. This is the first attempt to evaluate segmentation methodologies in echolocating bats and provides an evaluation framework that can be used on other species.

[1]  O. Ovaskainen,et al.  State-space models of individual animal movement. , 2008, Trends in ecology & evolution.

[2]  Andréa Thiebault,et al.  Bird-Borne Video-Cameras Show That Seabird Movement Patterns Relate to Previously Unrevealed Proximate Environment, Not Prey , 2014, PloS one.

[3]  Cynthia F. Moss,et al.  A 2.6‐g sound and movement tag for studying the acoustic scene and kinematics of echolocating bats , 2019 .

[4]  Kenady Wilson,et al.  Integrating multiple technologies to understand the foraging behaviour of Hawaiian monk seals , 2017, Royal Society Open Science.

[5]  Sara M. Maxwell,et al.  Foraging Behavior and Success of a Mesopelagic Predator in the Northeast Pacific Ocean: Insights from a Data-Rich Species, the Northern Elephant Seal , 2012, PloS one.

[6]  Cynthia F. Moss,et al.  Probing the Natural Scene by Echolocation in Bats , 2010, Front. Behav. Neurosci..

[7]  A. Hedenström,et al.  Bat flight: aerodynamics, kinematics and flight morphology , 2015, The Journal of Experimental Biology.

[8]  Lasse Jakobsen,et al.  How the bat got its buzz , 2013, Biology Letters.

[9]  David Pinaud,et al.  Quantifying search effort of moving animals at several spatial scales using first‐passage time analysis: effect of the structure of environment and tracking systems , 2007 .

[10]  Hans-Ulrich Schnitzler,et al.  Bat guilds, a concept to classify the highly diverse foraging and echolocation behaviors of microchiropteran bats , 2013, Front. Physiol..

[11]  Niko Balkenhol,et al.  Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns , 2016, Movement ecology.

[12]  Darcy R. Visscher,et al.  Identifying Movement States From Location Data Using Cluster Analysis , 2010 .

[13]  Mark E. Laidre,et al.  Principles of Animal Communication Principles of Animal Communication, 2nd edn. By JACK W. Bradbury & Sandra L. Vehrencamp. Sunderland, Massachusetts: Sinauer (2011). Pp. xiv + 697. Price $99.95. , 2012, Animal Behaviour.

[14]  Arjan Boonman,et al.  On-board recordings reveal no jamming avoidance in wild bats , 2015, Proceedings of the Royal Society B: Biological Sciences.

[15]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[16]  Antton Alberdi,et al.  Ecology and evolutionary biology of fishing bats , 2018, Mammal Review.

[17]  J. Kocik,et al.  Aquatic animal telemetry: A panoramic window into the underwater world , 2015, Science.

[18]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[19]  Stuart Bearhop,et al.  Search and foraging behaviors from movement data: A comparison of methods , 2017, Ecology and evolution.

[20]  Herbert H. T. Prins,et al.  Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat , 2015, PloS one.

[21]  Lutz Wiegrebe,et al.  Fast sensory–motor reactions in echolocating bats to sudden changes during the final buzz and prey intercept , 2015, Proceedings of the National Academy of Sciences.

[22]  Christopher C Wilmers,et al.  Behavior-specific habitat selection by African lions may promote their persistence in a human-dominated landscape. , 2019, Ecology.

[23]  Brett T. McClintock,et al.  momentuHMM: R package for generalized hidden Markov models of animal movement , 2017, 1710.03786.

[24]  Yossi Yovel,et al.  Resource Ephemerality Drives Social Foraging in Bats , 2018, Current Biology.

[25]  Hans-Ulrich Schnitzler,et al.  Fishing and echolocation behavior of the greater bulldog bat, Noctilio leporinus, in the field , 1994, Behavioral Ecology and Sociobiology.

[26]  Mark Hebblewhite,et al.  Statistical Methods for Identifying Wolf Kill Sites Using Global Positioning System Locations , 2008 .

[27]  Martin Wikelski,et al.  50 years of bat tracking: device attachment and future directions , 2014 .

[28]  Joachim M. Buhmann,et al.  The Balanced Accuracy and Its Posterior Distribution , 2010, 2010 20th International Conference on Pattern Recognition.

[29]  Juan M. Morales,et al.  EXTRACTING MORE OUT OF RELOCATION DATA: BUILDING MOVEMENT MODELS AS MIXTURES OF RANDOM WALKS , 2004 .

[30]  Frederic Bartumeus,et al.  Expectation-Maximization Binary Clustering for Behavioural Annotation , 2015, PloS one.

[31]  M. Obrist.  Flexible bat echolocation: the influence of individual, habitat and conspecifics on sonar signal design , 1995, Behavioral Ecology and Sociobiology.

[32]  R. Suthers,et al.  Comparative echolocation by fishing bats. , 1967, Journal of mammalogy.

[33]  Alice D. Domalik,et al.  At-sea distribution and fine-scale habitat use patterns of zooplanktivorous Cassin’s auklets during the chick-rearing period , 2018, Marine Biology.

[34]  Eliezer Gurarie,et al.  What is the animal doing? Tools for exploring behavioural structure in animal movements. , 2016, The Journal of animal ecology.

[35]  Yossi Yovel,et al.  Bats - Using Sound to Reveal Cognition , 2018 .

[36]  Clément Calenge,et al.  The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals , 2006 .

[37]  Sophie Bestley,et al.  Feeding ecology of wild migratory tunas revealed by archival tag records of visceral warming. , 2008, The Journal of animal ecology.

[38]  Inazio Garin,et al.  Insight on how fishing bats discern prey and adjust their mechanic and sensorial features during the attack sequence , 2015, Scientific reports.

[39]  Henri Weimerskirch,et al.  Are seabirds foraging for unpredictable resources , 2007 .

[40]  Torkild Tveraa,et al.  Hierarchical patch dynamics and animal movement pattern , 2006, Oecologia.

[41]  Arjan Boonman,et al.  Bats Aggregate to Improve Prey Search but Might Be Impaired when Their Density Becomes Too High , 2015, Current Biology.

[42]  Charles R. Michael,et al.  The echolocation of flying insects by bats , 1960 .

[43]  Jaime A. Ramos,et al.  How area restricted search of a pelagic seabird changes while performing a dual foraging strategy , 2010 .

[44]  David J. Ketchen,et al.  THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE , 1996 .

[45]  P S Segre,et al.  Using Digital Tags With Integrated Video and Inertial Sensors to Study Moving Morphology and Associated Function in Large Aquatic Vertebrates , 2017, Anatomical record.

[46]  Bruce T. Milne,et al.  Animal movements and population dynamics in heterogeneous landscapes , 1992, Landscape Ecology.

[47]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[48]  Eliezer Gurarie,et al.  A novel method for identifying behavioural changes in animal movement data. , 2009, Ecology letters.

[49]  Eliezer Gurarie,et al.  Correlated velocity models as a fundamental unit of animal movement: synthesis and applications , 2017, Movement ecology.

[50]  L. Börger.  EDITORIAL: Stuck in motion? Reconnecting questions and tools in movement ecology. , 2016, The Journal of animal ecology.

[51]  Antton Alberdi,et al.  Fine-tuned echolocation and capture-flight of Myotis capaccinii when facing different-sized insect and fish prey , 2014, Journal of Experimental Biology.

[52]  Torkild Tveraa,et al.  USING FIRST‐PASSAGE TIME IN THE ANALYSIS OF AREA‐RESTRICTED SEARCH AND HABITAT SELECTION , 2003 .

[53]  Avner Bar-Hen,et al.  Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data , 2012 .

[54]  Yossi Yovel,et al.  Using on-board sound recordings to infer behaviour of free-moving wild animals , 2019, Journal of Experimental Biology.

[55]  R. Kays,et al.  Terrestrial animal tracking as an eye on life and planet , 2015, Science.

[56]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[57]  E. Revilla,et al.  A movement ecology paradigm for unifying organismal movement research , 2008, Proceedings of the National Academy of Sciences.

[58]  L. Gerardo Herrera M.,et al.  Marine and terrestrial food sources in the diet of the fish-eating myotis (Myotis vivesi) , 2013 .

[59]  Henri Weimerskirch,et al.  Does Prey Capture Induce Area‐Restricted Search? A Fine‐Scale Study Using GPS in a Marine Predator, the Wandering Albatross , 2007, The American Naturalist.

[60]  D. Haydon,et al.  Multiple movement modes by large herbivores at multiple spatiotemporal scales , 2008, Proceedings of the National Academy of Sciences.