Skip to main content
Book cover

Machine Learning for Ecology and Sustainable Natural Resource Management pp 295–312Cite as

Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (Hydrobates leucorhous) Vocalizations

Abstract

Leach’s storm-petrel (Hydrobates leucorhous), a nocturnal seabird that breeds in the Northern hemisphere, has geographically separated populations in the North Atlantic and North Pacific. Although some mixing occurs during the non-breeding season, genetic evidence demonstrates that these populations are diverging. However, genetic information for the study of phylogenetics can be costly and time-consuming to obtain. Vocalizations could offer a more cost-effective way of obtaining similar information (or could be used in conjunction with it). In this chapter, we examine if the chatter call of the Leach’s storm-petrel can be used to classify the Atlantic and Pacific populations. We used a machine learning context by testing several implementations of random forests and boosted regression trees in the R and Python programming languages. We discuss the implementations with respect to accuracy, speed, and memory handling. We found that random forests from the h2o and ‘randomForest’ packages in R performed best with regards to accuracy, ‘randomForest’ and ‘gbm’ performing best with regards to speed, and ‘tensor forest’ and ‘h2o’ implementations performing best with regards to memory. Furthermore, we were able to classify the Atlantic versus Pacific populations of Leach’s storm-petrel with AUC values >0.8 (generally considered ‘good’ in ecology). We expect that this could be adapted on a larger scale to assist taxonomic classification without having to perform invasive DNA sampling on many individuals of sensitive populations.

Keywords

  • Vocalizations
  • Geographic variation
  • Boosted regression trees

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-96978-7_15
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-96978-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 15.1
Fig. 15.2
Fig. 15.3
Fig. 15.4
Fig. 15.5
Fig. 15.6

References

  • Acevedo MA, Corrada-Bravo CJ, Corrada-Bravo H, Villanueva-Rivera LJ, Aide TM (2009) Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Eco Inform 4:206–214

    CrossRef  Google Scholar 

  • Adkisson CS (1981) Geographic variation in vocalizations and evolution of North American Pine Grosbeaks. Condor 83:277

    CrossRef  Google Scholar 

  • Ainley DG (1980) Geographic variation in Leach’s storm-petrel. Auk 97:837–853

    Google Scholar 

  • Alström P, Ranft R (2003) The use of sounds in avian systematics and the importance of bird sound archives. Bull Br Ornithol Club 123A:114–135

    Google Scholar 

  • Arcese P, Sogge MK, Marr AB, Patten MA (2002) Song sparrow (Melospiza melodia), version 2.0. In: Poole AF, Gill FB (eds) The birds of North America. Cornell Lab of Ornithology, Ithaca

    Google Scholar 

  • Bicknell AWJ, Knight ME, Bilton D, Reid JB, Burke T, Votier SC (2012) Population genetic structure and long-distance dispersal among seabird populations: Implications for colony persistence. Mol Ecol 21:2863–2876

    CrossRef  CAS  Google Scholar 

  • Boncoraglio G, Saino N (2007) Habitat structure and the evolution of bird song: a meta-analysis of the evidence for the acoustic adaptation hypothesis. Funct Ecol 21:134–142

    CrossRef  Google Scholar 

  • Bourne WRP, Jehl JR Jr (1982) Variation and nomenclature of Leach’s storm-petrels. Auk 99:793–797

    Google Scholar 

  • Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159

    CrossRef  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    CrossRef  Google Scholar 

  • Breiman L, Cutler A (2007) Random forests-classification description

    Google Scholar 

  • Breiman L, Friedman J, Stone C, Olshen R (1984) Classification and regression trees. CRC Press, Boca Raton

    Google Scholar 

  • Bretagnolle V (1989) Calls of Wilson’s storm petrel: functions, individual and sexual recognitions, and geographic variation. Behaviour 111:98–112

    CrossRef  Google Scholar 

  • Bretagnolle V, Genevois F (1997) Geographic variation in the call of the blue petrel: effects of sex and geographical scale. Condor 99(4):985–989

    CrossRef  Google Scholar 

  • Bretagnolle V, Mougeot F, Genevois F (1998) Intra- and Intersexual Functions in the Call, of a Non-Passerine Bird. Behaviour 135(8):1161–1184

    CrossRef  Google Scholar 

  • Brooke M (2004) Albatrosses and petrels across the world. Oxford University Press, Oxford, p 499

    Google Scholar 

  • Buxton R, Major HL, Jones IL, Williams JC (2013) Examining patterns in nocturnal seabird activity and recovery across the western Aleutian Islands, Alaska, using automated acoustic recording. Auk 130(2):331–341

    CrossRef  Google Scholar 

  • Buxton RT, McKenna MF, Clapp M, Meyer E, Angeloni L, Crooks K, and Wittemyer G (2018) Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity. Conserv Biol

    Google Scholar 

  • Chen X, Ishwaran H (2012) Random forests for genomic data analysis. Genomics 99(6):323–329

    CrossRef  CAS  Google Scholar 

  • Colthurst T, Sculley D, Hendry G, Nado Z (2016) Tensorforest: scalable random forests on tensorflow, in: Machine learning systems workshop at NIPS

    Google Scholar 

  • Cutler D, Edwards T, Beard K, Cutler A, Hess K, Gibson J, Lawler J (2007) Random forests for classification in ecology. Ecology 88:2783–2792

    CrossRef  Google Scholar 

  • De’ath G, Fabricius K (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178–3192

    CrossRef  Google Scholar 

  • Elith J, Graham C, Anderson R, Dudik M, Ferrier S, Guisan A, Hijmans R, Huettmann F, Leathwick J, Lehmann A, Li J, Lohmann L, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J, Townsend Peterson A, Phillips S, Richardson K, Scachetti-pereira R, Schapire R, Williams S, Wisz M, Zimmermann N, Overton JMC, Soberon J (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

    CrossRef  Google Scholar 

  • Elith J, Leathwick J, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813

    CrossRef  CAS  Google Scholar 

  • Ferreira M, Ferguson JWH (2002) Geographic variation in the calling song of the field cricket Gryllus bimaculatus (Orthoptera: Gryllidae) and its relevance to mate recognition and mate choice. J Zool 257:163–170

    CrossRef  Google Scholar 

  • Friedman J (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378

    CrossRef  Google Scholar 

  • Garland EC, Goldizen AW, Rekdahl ML, Constantine R, Garrigue C, Hauser Nan D, Poole MM, Robbins J, Noad MJ (2011) Dynamic horizontal cultural transmission of humpback whale song at the ocean basin scale. Curr Biol 21:687–691

    CrossRef  CAS  Google Scholar 

  • H2O.ai (2016) R Interface for H2O

    Google Scholar 

  • Hamao S, Sugita N, Nishiumi I (2016) Geographic variation in bird songs: examination of the effects of sympatric related species on the acoustic structure of songs. Acta Ethologica 19:81–90

    CrossRef  Google Scholar 

  • Huang C-J, Yang Y-J, Yang D-X, Chen Y-J (2009) Frog classification using machine learning techniques. Expert Syst Appl 36:3737–3743

    CrossRef  Google Scholar 

  • Humphries GRW (2007) Leach’s storm-petrel (Oceanodrama leucorhoa) vocalizations: a comparison of chatter calls between Buldir Island, Aleutian Islands, Alaska and Gull Island, Newfoundland. B.Sc. Thesis, Memorial University of Newfoundland. 80 pp

    Google Scholar 

  • Huntington CE, Butler RG, Mauck RA (1996) Leach’s Storm-Petrel (Oceanodroma leucorhoa). In: Poole A, Gill F (eds) The birds of north America. Academy of Natural Sciences, Philidelphia, PA and the American Ornithologists’ Union, Washington DC

    Google Scholar 

  • Jones IL, Falls JB, Gaston AJ (1987) Vocal recognition between parents and young of Ancient Murrelets (Synthliboramphus antiquus). Anim Behav 35:1405–1415

    CrossRef  Google Scholar 

  • Keighley MV, Langmore NE, Zdenek CN, Heinsohn R (2017) Geographic variation in the vocalizations of Australian palm cockatoos (Probosciger aterrimus). Bioacoustics 26(1):91–108

    CrossRef  Google Scholar 

  • Kriesell HJ, Aubin T, Planas-Bielsa V, Benoiste M, Bonadonna F, Gachot-Neveu H, Le Maho Y, Schull Q, Vallas B, Zahn S, Le Bohec C (2018) Sex identification in King Penguins Aptenodytes patagonicus through morphological and acoustic cues. Ibis. https://doi.org/10.1111/ibi.12577

    CrossRef  Google Scholar 

  • Kroodsma D (2005) The singing life of birds: the art and science of listening to birdsong. Houghton Mifflin Company, New York

    Google Scholar 

  • Lambrechts MM, Dhondt AA (1995) Individual voice discrimination in birds. In: Power DM (ed) Curr Ornithol, vol 12. Springer, Boston

    Google Scholar 

  • Lynch MA, Lynch HJ (2017) Variation in the ecstatic display call of the Gentoo Penguin (Pygoscelis papua) across regional geographic scales. Auk 134(4):894–902

    CrossRef  Google Scholar 

  • Marler P, Slabbekoorn HW (2004) Nature’s music : the science of birdsong. Elsevier Academic, San Diego

    Google Scholar 

  • McCracken KG, Sheldon FH (1997) Avian vocalizations and phylogenetic signal. Proc Natl Acad Sci 94:3833–3836

    CrossRef  CAS  Google Scholar 

  • Mullet TC, Gage SH, Morton JM, Huettmann F (2016) Temporal and spatial variation of a winter soundscape in south-central Alaska. Landsc Ecol 31:1117–1137

    CrossRef  Google Scholar 

  • Nelson DA (2017) Geographical variation in song phrases differs with their function in white-crowned sparrow song. Anim Behav 124:263–271

    CrossRef  Google Scholar 

  • Petkov CI, Jarvis ED (2012) Birds, primates, and spoken language origins: behavioral phenotypes and neurobiological substrates. Front Evol Neurosci 4:12

    CrossRef  Google Scholar 

  • Picard RR, Cook RD (1984) Cross-validation of regression models. J Am Stat Assoc 79(387):575–583

    CrossRef  Google Scholar 

  • Pijanowski BC, Villanueva-Rivera LJ, Dumyahn SL, Farina A, Krause BL, Napoletano BM, Gage SH, Pieretti N (2011) Soundscape ecology: the science of sound in the landscape. BioScience 61:203–216

    CrossRef  Google Scholar 

  • Power DM, Ainley DG (1986) Seabird geographic variation: Similarity among populations of Leach’s storm-petrel. Auk 103:575–585

    Google Scholar 

  • Prasad A, Iverson L, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199

    CrossRef  Google Scholar 

  • Ridgeway G (2007) gbm: generalized boosted regression models. Documentation on the R Package “gbm”, version 1.5

    Google Scholar 

  • Roul S (2010) Distribution and status of the Manx Shearwater Puffinus puffinus on islands near the Burin Peninsula, Newfoundland in 2009, as inferred from automated acoustic monitoring devices. B.Sc Honours thesis, Memorial University of Newfoundland

    Google Scholar 

  • Schafer RM (1977) Tuning of the World. Knopf, New York

    Google Scholar 

  • Sosa-López JR, Martínez Gómez JE, Mennill DJ (2016) Divergence in mating signals correlates with genetic distance and behavioural responses to playback. J Evol Biol 29(2):306–318

    CrossRef  Google Scholar 

  • Southworth (1969) The sonic environment of the cities Environment and behavior I, 49±70

    Google Scholar 

  • Taoka M, Okumura H (1990) Sexual differences in flight calls and the cue for vocal sex recognition of Swinhoe’s storm-petrels. Condor 92:571

    CrossRef  Google Scholar 

  • Taoka M, Sato T, Kamada T, Okumura H (1989) Sexual dimorphism of chatter-calls and vocal sex recognition in Leach’s storm-petrels (Oceanodroma leucorhoa). Auk 106:498–501

    Google Scholar 

  • Warham J (1990) The petrels – their ecology and breeding systems. Academic Press, London, p 440

    Google Scholar 

  • Warren D, Seifert S (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342

    CrossRef  Google Scholar 

  • Watanuki Y (1986) Moonlight avoidance behavior in Leach’s storm-petrels as a defense against slaty-backed gulls. AUK 103:14–22

    Google Scholar 

  • Winker K (2010) Subspecies represent geographically partitioned variation, a gold mine of evolutionary biology, and a challenge for conservation. Ornithol Monogr 67(1):6

    CrossRef  Google Scholar 

  • Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG proceedings: health care and life sciences, Baltimore, Maryland, 19, p 67

    Google Scholar 

Download references

Acknowledgments

Thanks to S. Seneviratne for guidance with the original project in 2007 and to T. Miller for loan of the equipment used to record birds on Buldir and Gull islands. Thanks to A. Hedd and G. Robertson for field support, and for D. Fifield and S. Roul for providing some of the recordings used in our analysis.

Author information

Authors and Affiliations

  1. Black Bawks Data Science Ltd., Fort Augustus, Scotland

    Grant R. W. Humphries

  2. Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA

    Rachel T. Buxton

  3. Department of Biology, Memorial University, St. John’s, NL, Canada

    Ian L. Jones

Authors
  1. Grant R. W. Humphries
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rachel T. Buxton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ian L. Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Grant R. W. Humphries .

Editor information

Editors and Affiliations

  1. Black Bawks Data Science Ltd, Fort Augustus, Scotland, New Zealand

    Grant Humphries

  2. U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, USA

    Dawn R. Magness

  3. Institute of Arctic Biology, University of Alaska - Fairbanks, FAIRBANKS, AK, USA

    Falk Huettmann

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Humphries, G.R.W., Buxton, R.T., Jones, I.L. (2018). Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (Hydrobates leucorhous) Vocalizations. In: Humphries, G., Magness, D., Huettmann, F. (eds) Machine Learning for Ecology and Sustainable Natural Resource Management. Springer, Cham. https://doi.org/10.1007/978-3-319-96978-7_15

Download citation