Professional Education


  • Master of Science, University of Florida (2008)
  • Bachelor of Science, University of Florida (2006)
  • Doctor of Philosophy, Cornell University (2020)
  • Master of Arts, Columbia University (2011)

Stanford Advisors


All Publications


  • A machine learning approach for classifying and quantifying acoustic diversity METHODS IN ECOLOGY AND EVOLUTION Keen, S. C., Odom, K. J., Webster, M. S., Kohn, G. M., Wright, T. F., Araya-Salas, M. 2021
  • Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa. Biological reviews of the Cambridge Philosophical Society Odom, K. J., Araya-Salas, M., Morano, J. L., Ligon, R. A., Leighton, G. M., Taff, C. C., Dalziell, A. H., Billings, A. C., Germain, R. R., Pardo, M., de Andrade, L. G., Hedwig, D., Keen, S. C., Shiu, Y., Charif, R. A., Webster, M. S., Rice, A. N. 2021

    Abstract

    Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly increasing in sophistication such that choosing appropriate acoustic and evolutionary approaches is increasingly difficult. However, the correct choice of analysis can have profound effects on output and evolutionary inferences. Here, we identify and address some of the challenges for this growing field by providing a roadmap for quantifying and comparing sound in a phylogenetic context for researchers with a broad range of scientific backgrounds. Sound, as a continuous, multidimensional trait can be particularly challenging to measure because it can be hard to identify variables that can be compared across taxa and it is also no small feat to process and analyse the resulting high-dimensional acoustic data using approaches that are appropriate for subsequent evolutionary analysis. Additionally, terminological inconsistencies and the role of learning in the development of acoustic traits need to be considered. Phylogenetic comparative analyses also have their own sets of caveats to consider. We provide a set of recommendations for delimiting acoustic signals into discrete, comparable acoustic units. We also present a three-stage workflow for extracting relevant acoustic data, including options for multivariate analyses and dimensionality reduction that is compatible with phylogenetic comparative analysis. We then summarize available phylogenetic comparative approaches and how they have been used in comparative bioacoustics, and address the limitations of comparative analyses with behavioural data. Lastly, we recommend how to apply these methods to acoustic data across a range of study systems. In this way, we provide an integrated framework to aid in quantitative analysis of cross-taxa variation in animal sounds for comparative phylogenetic analysis. In addition, we advocate the standardization of acoustic terminology across disciplines and taxa, adoption of automated methods for acoustic feature extraction, and establishment of strong data archival practices for acoustic recordings and data analyses. Combining such practices with our proposed workflow will greatly advance the reproducibility, biological interpretation, and longevity of comparative bioacoustic studies.

    View details for DOI 10.1111/brv.12695

    View details for PubMedID 33652499

  • Social learning of acoustic anti-predator cues occurs between wild bird species PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES Keen, S. C., Cole, E. F., Sheehan, M. J., Sheldon, B. C. 2020; 287 (1920): 20192513

    Abstract

    In many species, individuals gather information about their environment both through direct experience and through information obtained from others. Social learning, or the acquisition of information from others, can occur both within and between species and may facilitate the rapid spread of antipredator behaviour. Within birds, acoustic signals are frequently used to alert others to the presence of predators, and individuals can quickly learn to associate novel acoustic cues with predation risk. However, few studies have addressed whether such learning occurs only though direct experience or whether it has a social component, nor whether such learning can occur between species. We investigate these questions in two sympatric species of Parids: blue tits (Cyanistes caeruleus) and great tits (Parus major). Using playbacks of unfamiliar bird vocalizations paired with a predator model in a controlled aviary setting, we find that blue tits can learn to associate a novel sound with predation risk via direct experience, and that antipredator response to the sound can be socially transmitted to heterospecific observers, despite lack of first-hand experience. Our results suggest that social learning of acoustic cues can occur between species. Such interspecific social information transmission may help to mediate the formation of mixed-species aggregations.

    View details for DOI 10.1098/rspb.2019.2513

    View details for Web of Science ID 000525806200002

    View details for PubMedID 32075532

    View details for PubMedCentralID PMC7031672

  • Acoustic monitoring for conservation in tropical forests: examples from forest elephants METHODS IN ECOLOGY AND EVOLUTION Wrege, P. H., Rowland, E. D., Keen, S., Shiu, Y. 2017; 8 (10): 1292–1301
  • Automated detection of low-frequency rumbles of forest elephants: A critical tool for their conservation JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA Keen, S. C., Shiu, Y., Wrege, P. H., Rowland, E. D. 2017; 141 (4): 2715–26

    Abstract

    African forest elephants (Loxodonta cyclotis) occupy large ranges in dense tropical forests and often use far-reaching vocal signals to coordinate social behavior. Elephant populations in Central Africa are in crisis, having declined by more than 60% in the last decade. Methods currently used to monitor these populations are expensive and time-intensive, though acoustic monitoring technology may offer an effective alternative if signals of interest can be efficiently extracted from the sound stream. This paper proposes an automated elephant call detection algorithm that was tested on nearly 4000 h of field recordings collected from five forest clearings in Central Africa, including sites both inside protected areas and in logging concessions. Recordings were obtained in different seasons, years, and under diverse weather conditions. The detector achieved an 83.2% true positive rate when the false positive rate is 5.5% (approximately 20 false positives per hour). These results suggest that this algorithm can enable analysis of long-term recording datasets or facilitate near-real-time monitoring of elephants in a wide range of settings and conditions.

    View details for DOI 10.1121/1.4979476

    View details for Web of Science ID 000400619400056

    View details for PubMedID 28464628

  • Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring PLOS ONE Salamon, J., Bello, J., Farnsworth, A., Robbins, M., Keen, S., Klinck, H., Kelling, S. 2016; 11 (11): e0166866

    Abstract

    Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants' flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research.

    View details for DOI 10.1371/journal.pone.0166866

    View details for Web of Science ID 000388889500040

    View details for PubMedID 27880836

    View details for PubMedCentralID PMC5120805

  • Can Nocturnal Flight Calls of the Migrating Songbird, American Redstart, Encode Sexual Dimorphism and Individual Identity? PLOS ONE Griffiths, E. T., Keen, S. C., Lanzone, M., Farnsworth, A. 2016; 11 (6): e0156578

    Abstract

    Bird species often use flight calls to engage in social behavior, for instance maintain group cohesion and to signal individual identity, kin or social associations, or breeding status of the caller. Additional uses also exist, in particular among migrating songbirds for communication during nocturnal migration. However, our understanding of the information that these vocalizations convey is incomplete, especially in nocturnal scenarios. To examine whether information about signaler traits could be encoded in flight calls we quantified several acoustic characteristics from calls of a nocturnally migrating songbird, the American Redstart. We recorded calls from temporarily captured wild specimens during mist-netting at the Powdermill Avian Research Center in Rector, PA. We measured call similarity among and within individuals, genders, and age groups. Calls from the same individual were significantly more similar to one another than to the calls of other individuals, and calls were significantly more similar among individuals of the same sex than between sexes. Flight calls from hatching-year and after hatching-year individuals were not significantly different. Our results suggest that American Redstart flight calls may carry identifiers of gender and individual identity. To our knowledge, this is the first evidence of individuality or sexual dimorphism in the flight calls of a migratory songbird. Furthermore, our results suggest that flight calls may have more explicit functions beyond simple group contact and cohesion. Nocturnal migration may require coordination among numerous individuals, and the use of flight calls to transmit information among intra- and conspecifics could be advantageous. Applying approaches that account for such individual and gender information may enable more advanced research using acoustic monitoring.

    View details for DOI 10.1371/journal.pone.0156578

    View details for Web of Science ID 000377564000016

    View details for PubMedID 27284697

    View details for PubMedCentralID PMC4902225

  • Song in a Social and Sexual Context: Vocalizations Signal Identity and Rank in Both Sexes of a Cooperative Breeder FRONTIERS IN ECOLOGY AND EVOLUTION Keen, S., Meliza, C., Pilowsky, J., Rubenstein, D. R. 2016; 4
  • A comparison of similarity-based approaches in the classification of flight calls of four species of North American wood-warblers (Parulidae) ECOLOGICAL INFORMATICS Keen, S., Ross, J. C., Griffiths, E. T., Lanzone, M., Farnsworth, A. 2014; 21: 25–33
  • Flight calls signal group and individual identity but not kinship in a cooperatively breeding bird BEHAVIORAL ECOLOGY Keen, S. C., Meliza, C., Rubenstein, D. R. 2013; 24 (6): 1279–85

    Abstract

    In many complex societies, intricate communication and recognition systems may evolve to help support both direct and indirect benefits of group membership. In cooperatively breeding species where groups typically comprise relatives, both learned and innate vocal signals may serve as reliable cues for kin recognition. Here, we investigated vocal communication in the plural cooperatively breeding superb starling, Lamprotornis superbus, where flight calls-short, stereotyped vocalizations used when approaching conspecifics-may communicate kin relationships, group membership, and/or individual identity. We found that flight calls were most similar within individual repertoires but were also more similar within groups than within the larger population. Although starlings responded differently to playback of calls from their own versus other neighboring and distant social groups, call similarity was uncorrelated with genetic relatedness. Additionally, immigrant females showed similar patterns to birds born in the study population. Together, these results suggest that flight calls are learned signals that reflect social association but may also carry a signal of individuality. Flight calls, therefore, provide a reliable recognition mechanism for groups and may also be used to recognize individuals. In complex societies comprising related and unrelated individuals, signaling individuality and group association, rather than kinship, may be a route to cooperation.

    View details for DOI 10.1093/beheco/art062

    View details for Web of Science ID 000325995900002

    View details for PubMedID 24137044

    View details for PubMedCentralID PMC3796708