A comparison of supervised learning techniques in the classification of bat echolocation calls

Ecological Informatics

Volumn 5, Issue 6, 2010, Pages 465-473

  Armitage, David W ^a   Ober, Holly K ^b  

^a UNIVERSITY OF FLORIDA   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

Acoustics; Bat echolocation; Frequency division; Machine learning

Indexed keywords

BAT; BIOACOUSTICS; COMPARATIVE STUDY; ECHOLOCATION; IMAGE CLASSIFICATION; SPECIES CONSERVATION; VOCALIZATION; WILDLIFE MANAGEMENT;

ANIMALIA; MYOTIS;

EID: 77957684003     PISSN: 15749541     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecoinf.2010.08.001     Document Type: Article

References (77)

1. Aanensen D.M., Huntley D.M., Feil E.J., al-Own F., Spratt B.G. EpiCollect: linking smartphones to web applications for epidemiology, ecology and community data collection. PLoS One 2009, 4(9):e6968. 10.1371/journal.pone.0006968.
2. Abe S. Support Vector Machines for Pattern Classification 2005, Springer-Verlag, London, UK.
3. Acevedo M.A., Corrada-Bravo C.J., Corrada-Bravo H., Villanueva-Rivera L.J., Aide T.M. Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Ecological Informatics 2009, 4:206-214.
4. Andersen B.B., Miller L.A. Portable ultrasonic-detection system for recording bat cries in the field. Journal of Mammalogy 1977, 58:226-229.
5. Anguita D., Ridella S., Sterpi D. A new method for multiclass support vector machines. Proceedings of International Joint Conference on Neural Networks (IJCNN 2004). Budapest Hungary 2004, 1:407-412.
6. Archer K.J., Kimes R.V. Empirical characterization of random forest variable importance measures. Computational Statistics and Data Analysis 2007, 52:2249-2260.
7. Armitage, D.W., 2010. The effects of prescribed fire on bat activity in the longleaf pine sandhills ecosystem. M.S. thesis, University of Florida, Gainesville, FL, USA.
8. Bao L., Cui Y. Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information. Bioinformatics 2005, 21:2185-2190.
9. Barclay R.M.R. Bats are not birds-a cautionary note on using echolocation calls to identify bats: a comment. Journal of Mammalogy 1999, 80:290-296.
10. Best T., Harvey M., Altenbach J. Batcall: Accoustic library and species accounts (on-line) Accessed July 28, 2010 at. http://www.msb.unm.edu/mammals/batcall/html/speciesaccounts.html.
11. Betts B.J. Effects of interindividual variation in echolocation calls on identification of big brown and silver-haired bats. Journal of Wildlife Management 1998, 62:1003-1010.
12. Biscardi S., Orprecio J., Fenton M.B., Tsoar A., Ratcliffe J.M. Data, sample sizes, and statistics affect the recognition of species of bats by their echolocation calls. Acta Chiropterologica 2004, 6:347-363.
13. Block B.A., Dewar H., Farwell C., Prince E.D. A new satellite technology for tracking the movements of Atlantic bluefin tuna. Proceedings of the National Academy of Sciences 1998, 95:9384-9389.
14. Breiman L. Random forests. Machine Learning 2001, 45:5-32.
15. Britzke, E.R., 2003. Use of ultrasonic detectors for acoustic identification and study of bat ecology in the eastern United States. Ph.D. dissertation, Tennessee Technological University, Cookeville, TN, USA.
16. Britzke E.R., Murray K.L. A quantitative method for selection of identifiable search-phase calls using the Anabat system. Bat Research News 2000, 41:33-36.
17. Caputo B., Sim K., Fursejo F., Smola A. Appearance-based object recognition using SVMs: which kernel should I use?. Proceedings of NIPS workshop on Statistical Methods for Computational Experiments in Visual Processing and Computer Vision, Whistler 2002.
18. Corben C. Zero-crossings analysis for bat identification: an overview. Bat Echolocation Research: Tools, Techniques, Analysis 2004, 95-106. Bat Conservation International, Austin, TX, USA. R.M. Brigham, E.K.V. Kalko, G. Jones, S. Parsons, H.J.G.A. Limpens (Eds.).
19. Corben C., Fellers G.M. Choosing the 'correct' bat detector-a reply. Acta Chiropterologica 2001, 3:253-256.
20. Corcoran, A.J., 2007. Automated acoustic identification of nine bat species of the eastern United States. M.S. thesis, Humboldt State University, Arcata, CA, USA.
21. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000, Cambridge University Press, Cambridge, UK.
22. Cutler D.R., Edwards T.C., Beard K.H., Cutler A., Hess K.T., Gibson J., Lawler J.J. Random forests for classification in ecology. Ecology 2007, 88:2783-2792.
23. Diamantaras K.I., Kung S.Y. Principal Components Neural Networks: Theory and Applications 1996, John Wiley and Sons, inc., New York, USA.
24. Drake J.M., Randin C., Guisan A. Modelling ecological niches with support vector machines. Journal of Applied Ecology 2006, 43:424-432.
25. Erickson J.L., West S.D. Associations of bats with local structure and landscape features of forested stands in western Oregon and Washington. Biological Conservation 2003, 109:95-102.
26. Fagerlund, S., in press. Bird species recognition using support vector machines. EURASIP Journal on Advances in Signal Processing. Article ID 38637, doi:10.1155/2007/38637.
27. Fenton M.B. A technique for monitoring bat activity with results obtained from different environments in southern Ontario. Canadian Journal of Zoology 1970, 48:847-851.
28. Fenton M.B. Choosing the 'correct' bat detector. Acta Chiropterologica 2000, 2:215-224.
29. Fenton M.B., Bell G.P. Recognition of species of insectivorous bats by their echolocation calls. Journal of Mammalogy 1981, 62:233-243.
30. Fielding A.H. How should accuracy be measured?. Machine Learning Methods for Ecological Applications 1999, 209-224. Kluwer Academic Publishers, Boston, MA, USA. A.H. Fielding (Ed.).
31. Fukui D., Agetsuma N., Hill D.A. Acoustic identification of eight species of bat (Mammalia: Chiroptera) inhabiting forests of southern Hokkaido, Japan: potential for conservation monitoring. Zoological Science 2004, 21:947-955.
32. Gannon W.L., O'Farrell M.J., Corben C., Bedrick E.J. Call character lexicon and analysis of field recorded bat echolocation calls. Echolocation In Bats and Dolphins 2004, 478-484. University of Chicago Press, Chicago, Illinois. J. Thomas, C. Moss, M. Vater (Eds.).
33. Hair J.F., Black W.C., Babin B.J., Anderson R.E., Tatham R.L. Multivariate Data Analysis 2006, Pearson Education Inc., Upper Saddle River, NJ, USA.
34. Hayes J.P., Ober H.K., Sherwin R.E. Survey and monitoring of bats. Ecological and Behavioral Methods for the Study of Bats 2009, 112-129. Johns Hopkins University Press, Baltimore, MD, USA. second ed. T.H. Kunz, S. Parsons (Eds.).
35. Herr A., Klomp N.I., Atkinson J.S. Identification of bat echolocation calls using a decision tree classification system. Complexity International 1997, 4:1-14.
36. Huang C., Yang Y., Yang D., Chen Y. Frog classification using machine learning techniques. Expert Systems with Applications 2009, 36:3737-3743.
37. Humes M.L., Hayes J.P., Collopy M.W. Bat activity in thinned, unthinned, and old-growth forests in western Oregon. Journal of Wildlife Management 1999, 63:533-561.
38. Hurlbert S.H. Pseudoreplication and the design of ecological field experiments. Ecological Monographs 1984, 54:187-211.
39. Jennings N., Parsons S., Pocock M.J.O. Human vs. machine: identification of bat species from their echolocation calls by humans and by artificial neural networks. Canadian Journal of Zoology 2008, 86:371-377.
40. Kalcounis M.C., Hobson K.A., Brigham R.M., Hecker K.R. Bat activity in the boreal forest: importance of stand type and vertical strata. Journal of Mammalogy 1999, 80:673-682.
41. Karanth K.U., Nichols J.D. Estimation of tiger densities in India using photographic captures and recaptures. Ecology 1998, 79:2852-2862.
42. Kazial K.A., Burnett S.C., Masters W.M. Individual and group variation in echolocation calls of big brown bats (Eptesicus fuscus) (Chiroptera: Verspertilionidae). Journal of Mammalogy 2001, 82:339-351.
43. Kirkhope C.L., Williams R.L., Catlin-Groves C.L., Rees S.G., Montesanti C., Jowers J., Stubbs H., Newberry J., Hart A.G., Goodenough A.E., Stafford R. Social networking for biodiversity: the BeeID project. International Conference on Information Society, June 28-30, 2010, London, U.K 2010, 637-638.
44. Krusic R.A., Neefus C.D. Habitat associations of bat species in the White Mountain National Forest. Bats and forests symposium, October 19-21, 1995 1996, 185-198. Victoria, British Columbia, Canada. R.M.R. Barclay, R.M. Brigham (Eds.).
45. Kuhn T.S. The Structure of Scientific Revolutions 1970, The University of Chicago Press, Chicago, IL, USA. second edition.
46. Kuhn M. Building predictive models in R using the caret package. Journal of Statistical Software 2008, 28:1-26.
47. Kuhn M. caret: Classification and Regression Training R package version 4.30. http://cran.r-project.org/web/packages/caret/index.html.
48. Laiolo P. The emerging significance of bioacoustics in animal species conservation. Biological Conservation 2010, 143:1635-1645.
49. Lance R.F., Bollich B., Callahan C.L., Leberg P.L. Surveying forest-bat communities with Anabat detectors. Bats and forests symposium, October 19-21, 1995 1996, 175-184. Victoria, British Columbia, Canada. R.M.R. Barclay, R.M. Brigham (Eds.).
50. Lek S., Giraudel J.L., Guégan J.F. Neuronal networks: algorithms and architectures for ecologists and evolutionary ecologists. Artificial Neuronal Networks: Application to Ecology and Evolution 2000, 3-27. Springer-Verlag, Berlin, Germany. S. Lek, J.F. Guégan (Eds.).
51. Limpens H.J.G.A. Choosing a bat detector: theoretical and practical aspects. Bat Echolocation Research: Tools, Techniques, Analysis 2004, 28-37. Bat Conservation International, Austin, TX, USA. R.M. Brigman, E.K.V. Kalko, G. Jones, S. Parsons, H.J.G.A. Limpens (Eds.).
52. Marks C.S., Marks G.E. Bats of Florida 2006, University Press of Florida, Gainesville, FL, USA.
53. Menzel J.M., Menzel M.A., Kligo J.C., Ford W.M., Edwards J.W., McCracken G.F. Effect of habitat and foraging height on bat activity in the coastal plain of South Carolina. Journal of Wildlife Management 2005, 69:235-245.
54. Meyer D., Leisch F., Hornik K. The support vector machine under test. Neurocomputing 2003, 55:169-186.
55. Mukherjee S. Classifying Microarray Data Using Support Vector Machines, Understanding And Using Microarray Analysis Techniques: A Practical Guide 2006, Kluwer Academic Publishers, Boston, MA, USA.
56. Mundry R., Sommer C. Discriminant function analysis with nonindependent data: consequences and an alternative. Animal Behaviour 2007, 74:965-976.
57. Nicodemus K.K., Malley J.D. Predictor correlation impacts machine learning algorithms: implications for genomic studies. Bioinformatics 2009, 25:1884-1890.
58. O'Farrell M.J., Miller B.W., Gannon W.L. Qualitative identification of free-flying bats using the Anabat detector. Journal of Mammalogy 1999, 80:11-23.
59. O'Farrell M.J., Corben C., Miller B.W., Gannon W.L. Confronting the dogma: a reply. Journal of Mammalogy 1999, 80:297-302.
60. O'Farrell M.J., Corben C., Gannon W.L. Geographic variation in the echolocation calls of the hoary bat (Lasiurus cinereus). Acta Chiropterologica 2000, 2:185-196.
61. Obrist M.K., Boesch R., Flückiger P.F. Variability in echolocation call design of 26 Swiss bat species: consequences, limits, and options for automated field identification with a synergetic pattern recognition approach. Mammalia 2004, 68:307-322.
62. Parsons S., Jones G. Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks. Journal of Experimental Biology 2000, 203:2641-2656.
63. Parsons S., Szewczak J.M. Detecting, recording, and analyzing the vocalizations of bats. Ecological and Behavioral Methods for the Study of Bats 2009, 91-111. Johns Hopkins University Press, Baltimore, MD, USA. second ed. T.H. Kunz, S. Parsons (Eds.).
64. Preatoni D.G., Nodari M., Chirichella R., Tosi G., Wauters L.A., Martinoli A. Identifying bats from time-expanded recordings of search calls: comparing classification methods. Journal of Wildlife Management 2005, 69:1601-1614.
65. R: A language and environment for statistical computing http://www.r-project.org/] 2009, R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria.
66. Redgwell R.D., Szewczak J.M., Jones G., Parsons S. Classification of echolocation calls from 14 species of bat by support vector machines and ensembles of neural networks. Algorithms 2009, 2:907-924.
67. Ripley B.D. Pattern Recognition and Neural Networks 1996, Cambridge University Press, Cambridge, UK.
68. Russo D., Jones G. Identification of twenty-two bat species (Mammalia: Chiroptera) from Italy by analysis of time-expanded recordings of echolocation calls. Journal of the Zoological Society of London 2002, 258:91-103.
69. Samarasinghe S. Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition 2007, Taylor and Francis Group, LLC, Boca Raton, FL, USA.
70. Skowronski M.D., Fenton M.B. Quantifying bat call detection performance of humans and machines. Journal of the Acoustical Society of America 2009, 125:513-521.
71. Skowronski M.D., Harris J.G. Acoustic detection and classification of microchiroptera using machine learning: lessons learned from automatic speech recognition. Journal of the Acoustic Society of America 2006, 119:1817-1833.
72. Stocker R.C. The role of artificial neural networks in the analysis of ultrasonic bat calls: a case study. Complexity International 1998, 5:1-15.
73. Vaughan N., Jones G., Harris S. Identification of British bat species by multivariate analysis of echolocation call parameters. Bioacoustics 1997, 7:189-207.
74. Veelenturf L.P.J. Analysis and Applications of Artificial Neural Networks 1995, Prentice Hall International, Ltd., Hertfordshire, UK.
75. Weller T.J., Lee D.C. Mist net effort required to inventory a forest bat assemblage. Journal of Wildlife Management 2007, 71:251-257.
76. White H. Artificial Neural Networks: Approximation and Learning Theory 1992, Blackwell, Cambridge, MA, USA.
77. Zhu J., Zou H. Variable selection for the linear support vector machine. Trends in Neural Computation, Studies in Computation Intelligence 35 2007, 35-60. Springer-Verlag, Berlin, Germany. K. Chen, L. Wang (Eds.).