A continental-scale tool for acoustic identification of European bats

Summary 1. Acoustic methods are used increasingly to survey and monitor bat populations. However, the use of acoustic methods at continental scales can be hampered by the lack of standardized and objective methods to identify all species recorded. This makes comparable continent-wide monitoring difficult, impeding progress towards developing biodiversity indicators, transboundary conservation programmes and monitoring species distribution changes. 2. Here we developed a continental-scale classifier for acoustic identification of bats, which can be used throughout Europe to ensure objective, consistent and comparable species identifications. We selected 1350 full-spectrum reference calls from a set of 15 858 calls of 34 European species, from EchoBank, a global echolocation call library. We assessed 24 call parameters to evaluate how well they distinguish between species and used the 12 most useful to train a hierarchy of ensembles of artificial neural networks to distinguish the echolocation calls of these bat species. 3. Calls are first classified to one of five call-type groups, with a median accuracy of 97·6%. The median species-level classification accuracy is 83·7%, providing robust classification for most European species, and an estimate of classification error for each species. 4. These classifiers were packaged into an online tool, iBatsID, which is freely available, enabling anyone to classify European calls in an objective and consistent way, allowing standardized acoustic identification across the continent. 5. Synthesis and applications. iBatsID is the first freely available and easily accessible continental- scale bat call classifier, providing the basis for standardized, continental acoustic bat monitoring in Europe. This method can provide key information to managers and conservation planners on distribution changes and changes in bat species activity through time.

[1]  B. Siemers,et al.  The communicative potential of bat echolocation pulses , 2011, Journal of Comparative Physiology A.

[2]  C. S. Robbins,et al.  The Breeding Bird Survey: Its First Fifteen Years, 1965-1979 , 1987 .

[3]  S. Puechmaille,et al.  White-nose syndrome: is this emerging disease a threat to European bats? , 2011, Trends in ecology & evolution.

[4]  H. Temple,et al.  The Status and Distribution of European Mammals , 2007 .

[5]  Norman MacLeod,et al.  Landmarks, Localization, and the Use of Morphometrics in Phylogenetic Analysis , 2001 .

[6]  S. Harris,et al.  Identification of British bat species by multivariate analysis of echolocation call parameters , 1997 .

[7]  Eleni Papadatou,et al.  Identification of bat species in Greece from their echolocation calls , 2008 .

[8]  Gareth Jones,et al.  The evolution of echolocation in bats. , 2006, Trends in ecology & evolution.

[9]  H. Schnitzler,et al.  From spatial orientation to food acquisition in echolocating bats , 2003 .

[10]  E. D. Chesmore,et al.  Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition , 2004, Bulletin of Entomological Research.

[11]  S. Puechmaille,et al.  A critical assessment of the presence of Barbastella barbastellus and Nyctalus noctula in Ireland with a description of N. leisleri echolocation calls from Ireland. , 2011 .

[12]  Donald R. Griffin,et al.  Experimental Determination of Supersonic Notes Emitted by Bats , 1938 .

[13]  S. Parsons,et al.  Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks. , 2000, The Journal of experimental biology.

[14]  Andreas M. Ali,et al.  Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus , 2011 .

[15]  Gareth Jones,et al.  Identification of twenty‐two bat species (Mammalia: Chiroptera) from Italy by analysis of time‐expanded recordings of echolocation calls , 2002 .

[16]  B. Siemers,et al.  Is species identity, sex, age or individual quality conveyed by echolocation call frequency in European horseshoe bats? , 2005 .

[17]  Kate E. Jones,et al.  Indicator bats program: A system for the global acoustic monitoring of bats , 2013 .

[18]  David W. Armitage,et al.  A comparison of supervised learning techniques in the classification of bat echolocation calls , 2010, Ecol. Informatics.

[19]  M. Fenton,et al.  Recognition of Species of Insectivorous Bats by Their Echolocation Calls , 1981 .

[20]  G. Yohe,et al.  A globally coherent fingerprint of climate change impacts across natural systems , 2003, Nature.

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

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

[23]  M. Brock Fenton,et al.  Evolution of Echolocation , 2013 .

[24]  Ingemar Ahlén,et al.  Use of ultrasound detectors for bat studies in Europe: experiences from field identification, surveys, and monitoring , 1999 .

[25]  Gareth Jones,et al.  Predicted impact of climate change on European bats in relation to their biogeographic patterns , 2010 .

[26]  M. Obrist,et al.  Variability in echolocation call design of 26 Swiss bat species: consequences, limits and options for automated field identification with a synergetic pattern recognition approach , 2004 .

[27]  H. Schnitzler,et al.  Echolocation by Insect-Eating Bats , 2001 .

[28]  M. Farrell,et al.  Contribution of acoustic methods to the study of insectivorous bat diversity in protected areas from northern Venezuela , 2000 .

[29]  Daryl E. Wilson,et al.  Mammal Species of the World: A Taxonomic and Geographic Reference , 1993 .

[30]  B. Richter,et al.  Biodiversity Conservation at Multiple Scales: Functional Sites, Landscapes, and Networks , 2000 .

[31]  D. Preatoni,et al.  IDENTIFYING BATS FROM TIME-EXPANDED RECORDINGS OF SEARCH CALLS: COMPARING CLASSIFICATION METHODS , 2005 .

[32]  M. Willig,et al.  Carpe noctem: the importance of bats as bioindicators , 2009 .

[33]  M. J. O'farrell,et al.  A Comparison of Acoustic Versus Capture Techniques for the Inventory of Bats , 1999 .

[34]  Peter E. Zingg Akustische Artidentifikation von Fledermäusen (Mammalia: Chiroptera) in der Schweiz , 1990 .

[35]  Klaus-Gerhard Heller,et al.  Resource partitioning of sonar frequency bands in rhinolophoid bats , 1989, Oecologia.

[36]  Gareth Jones,et al.  Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks , 2009, Algorithms.

[37]  S. Puechmaille,et al.  The evolution of sensory divergence in the context of limited gene flow in the bumblebee bat , 2011, Nature communications.

[38]  M. Kéry,et al.  Estimating species richness: calibrating a large avian monitoring programme , 2005 .

[39]  Kate E. Jones,et al.  Bat life histories:: Testing models of mammalian life-history evolution , 2001 .

[40]  H. Schnitzler,et al.  Plasticity in echolocation signals of European pipistrelle bats in search flight: implications for habitat use and prey detection , 1993, Behavioral Ecology and Sociobiology.

[41]  Paul A. Racey,et al.  What you see is not what you get : the role of ultrasonic detectors in increasing inventory completeness in Neotropical bat assemblages , 2008 .

[42]  E. Britzke,et al.  VARIATION IN SEARCH-PHASE CALLS OF BATS , 2001 .

[43]  E. Teeling,et al.  THE SHAPE OF SOUND: ELLIPTIC FOURIER DESCRIPTORS (EFD) DISCRIMINATE THE ECHOLOCATION CALLS OF MYOTIS BATS (M. DAUBENTONII, M. NATTERERI AND M. MYSTACINUS) , 2011 .

[44]  H. Pereira,et al.  Towards the global monitoring of biodiversity change. , 2006, Trends in ecology & evolution.

[45]  Peter K. McGregor,et al.  Census and monitoring based on individually identifiable vocalizations: the role of neural networks , 2002 .

[46]  Danilo Russo,et al.  Divergent echolocation call frequencies in insular rhinolophids (Chiroptera): a case of character displacement? , 2007 .

[47]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[48]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[49]  H L Roitblat,et al.  The neural network classification of false killer whale (Pseudorca crassidens) vocalizations. , 1998, The Journal of the Acoustical Society of America.