Research ArticleECOLOGY

Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology

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Science Advances  20 Jun 2018:
Vol. 4, no. 6, eaaq1084
DOI: 10.1126/sciadv.aaq1084
  • Fig. 1 Outline of bioacoustic methodology.

    We present two analytical approaches, supervised and unsupervised classifications. Both approaches rely on the same initial statistical characterization of the acoustic data set to identify songbird vocalizations, regardless of species. The supervised approach used a linear classifier to classify every 4-s segment of the data set for the presence/absence of songbird vocalizations, trained on a subset of listener-determined scores (<1% of data set). We used the proportion of segments per day containing songbird vocalizations as a relative score, referred to as the VAI. We estimated the arrival dates as the first date that exceeded 50% of the maximum value of the VAI. The unsupervised approach used a series of signal processing and machine learning techniques to cluster the acoustic data into potential physical sources (for example, vocalizations, wind, and trucks) without training from listener input. Because the number of physical sources is not known a priori, we initially clustered the data into 100 clusters. We then performed principal components analysis on the histograms of cluster assignments to reduce data to 20 dimensions. We estimated the arrival dates as the optimal segmentation boundary in principal components, as measured by the fit of Gaussian distributions on either side of the boundary (see the Supplementary Materials).

  • Fig. 2 Songbird community vocal activity estimated by supervised and unsupervised approaches.

    (A to E) Songbird daily VAI, snow cover (blue), and air temperature (red) near TLFS between 2010 and 2014. (F to J) Weighted sums of the first five principal components at the same site and time. Gray boxes identify the available recording period for acoustic data. Daily VAI and weighted sums for the entire data set at all field sites can be found in the Supplementary Materials (figs. S3 to S5).

  • Fig. 3 Influence of environmental conditions and breeding phenology on a songbird community vocal activity.

    Proportion of variance in the VAI explained by environmental covariates, as determined by linear models. To identify environmental covariates that were significantly predictive (P < 0.1) of the VAI, we used stepwise regression with backward variable selection based on a F test to build linear models for each recording period independently. We also built single-variable linear models with each environmental covariate in isolation. We built the same suite of linear models for the period before and after egg laying dates for the two most abundant songbird species. Points represent mean R2 (across sites and years) ± SE. Black circles indicate linear models built with data over the entire 30-day study period. Red triangles and blue squares indicate linear models built considering the period before and after the mean egg laying dates, respectively.

  • Fig. 4 Acoustically derived estimates of songbird arrival to breeding grounds and relationship to snow-free dates.

    (A) Songbird community mean arrival dates to their breeding grounds near TLFS, Alaska over a 5-year period (2010–2014) using supervised and unsupervised bioacoustic approaches compared to traditional avian surveys. SE bars reflect averages across four recording sites for acoustically derived estimates. (B) Songbird community arrival dates for at each site over a 5-year period (2010–2014) estimated from supervised and unsupervised bioacoustics approaches compared to the date on which the landscape surrounding the recording unit fell below 10% snow coverage.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/6/eaaq1084/DC1

    Supplementary Text

    fig. S1. Map of Alaska (inset) and TLFS with approximate locations of acoustic recording units.

    fig. S2. Performance of supervised and unsupervised classification approaches.

    fig. S3. Songbird community vocal activity estimated by supervised and unsupervised approaches near IMVT.

    fig. S4. Songbird community vocal activity estimated by supervised and unsupervised approaches near ROMO.

    fig. S5. Songbird community vocal activity estimated by supervised and unsupervised approaches near SDOT.

    fig. S6. Comparison of the VAI to linear model predictions using only environmental covariates found to be statistically significant.

    fig. S7. Threshold sensitivity of arrival date estimates from supervised approach.

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Text
    • fig. S1. Map of Alaska (inset) and TLFS with approximate locations of acoustic recording units.
    • fig. S2. Performance of supervised and unsupervised classification approaches.
    • fig. S3. Songbird community vocal activity estimated by supervised and unsupervised approaches near IMVT.
    • fig. S4. Songbird community vocal activity estimated by supervised and unsupervised approaches near ROMO.
    • fig. S5. Songbird community vocal activity estimated by supervised and unsupervised approaches near SDOT.
    • fig. S6. Comparison of the VAI to linear model predictions using only environmental covariates found to be statistically significant.
    • fig. S7. Threshold sensitivity of arrival date estimates from supervised approach.

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