Research Article

Fat, weather, and date affect migratory songbirds’ departure decisions, routes, and time it takes to cross the Gulf of Mexico

Jill L. Deppe, Michael P. Ward, Rachel T. Bolus, Robert H. Diehl, Antonio Celis-Murillo, Theodore J. Zenzal Jr., Frank R. Moore, Thomas J. Benson, Jaclyn A. Smolinsky, Lynn N. Schofield, David A. Enstrom, Eben H. Paxton, Gil Bohrer, Tara A. Beveroth, Arlo Raim, Renee L. Obringer, David Delaney, and William W. Cochran
PNAS November 17, 2015 112 (46) E6331-E6338; first published November 2, 2015 https://doi.org/10.1073/pnas.1503381112

  1. Edited by Martin Wikelski, Max Planck Institute for Ornithology, Radolfzell, Germany, and accepted by the Editorial Board September 30, 2015 (received for review February 18, 2015)

Article Figures & SI

Figures

  • Fig. 1.
    Fig. 1.

    Locations of automated telemetry towers around the Gulf of Mexico. (A) Locations of our capture site (blue arrow) and tracking towers in AL (black circles). (B) Locations of tracking towers along the northern Yucatan Peninsula (black circles). The distance between the two regions ranges from 950 to 1,040 km.

  • Fig. S1.
    Fig. S1.

    (A) Locations of automated tracking towers (solid black circles) around Mobile Bay in coastal Alabama. (B) Inset from A showing the location of tracking towers 1–4 (solid black circles) and the capture location (yellow asterisk) on the Fort Morgan Peninsula.

  • Fig. 2.
    Fig. 2.

    Percentage of birds selecting each departure decision at our coastal AL study site within 24 h of capture.

  • Fig. S2.
    Fig. S2.

    Circular plots illustrating the distribution of departure bearings of (A) Red-eyed Vireo, (B) Swainson’s Thrush, and (C) Wood Thrush.

  • Fig. S3.
    Fig. S3.

    Circular plots illustrating the distribution of departure times (Central Standard Time) for (Left) Red-eyed Vireo, (Center) Swainson’s Thrush, and (Right) Wood Thrush.

  • Fig. 3.
    Fig. 3.

    (A) Classification and regression tree illustrating predicted classification of birds’ departure decisions: stopover at coastal AL site for ≥24 h, over-land departure, or over-water departure. Box plots illustrate median and quartiles for variables predicting departure decision from AL: (B) fat scores, (C) humidity, and (D) 24-h change in humidity at sunset on the evening of capture. Negative values for change in humidity denote a drop in humidity, whereas positive values denote an increase in humidity. Sample sizes refer to number of individuals predicted in each class and the proportion reflects the accuracy of classification to each departure group.

  • Fig. 4.
    Fig. 4.

    Percentage of radio-tagged birds in each departure category that were detected in the Yucatan Peninsula. Sample sizes for each departure category within each of the three focal species are noted above bars.

  • Fig. 5.
    Fig. 5.

    (A) CART illustrating classification of birds among three arrival groups: birds that were not detected in the YP and those that arrived via indirect (>70 h following departure) and direct flights (<35 h following departure). Our CART of arrival at the YP includes only birds that departed over water on the evening of capture. Box plots illustrate median and quartiles for variables predicting arrival at the YP: (B) departure ordinal date, (C) fat score, and (D) wind profit. Positive wind-profit values denote headwinds, whereas negative values denote tailwinds. Larger-magnitude values indicate stronger wind speeds. Weather variables were retrieved for the date and time of departure.

  • Fig. S4.
    Fig. S4.

    Relationship between flight time (direct flights only) and mean wind-profit index. Tailwinds are denoted by positive values, whereas headwinds are denoted by negative values. Larger values indicate stronger winds.

  • Fig. S5.
    Fig. S5.

    Distribution of fat scores upon initial capture and tagging for the three focal species. A GLM with a multinomial distribution and generalized logit link function and capture data included as a random effect showed that Swainson’s Thrush had significantly lower average fat scores (mean ± SE: 2.89 ± 0.19) than both Red-eyed Vireos (3.47 ± 0.26) and Wood Thrush (4.02 ± 0.48; F2,191 = 4.29, P = 0.0151). Average fat scores were similar for REVIs and WOTH. We attempted to tag birds across fat classes in the three species.

Tables

  • Table S1.

    Correlation matrix among weather variables used in CART analysis of departure decisions

    VariableWind profitTemperatureHumidityPressureHumidity changePressure change
    Calendar dayr = −0.11r = −0.81r = −0.35r = 0.15r = −0.08r = −0.16
    P = 0.08P < 0.0001P < 0.0001P = 0.02P = 0.20P = 0.01
    Wind profitr = 0.09r = −0.34r = 0.02r = −0.16r = −0.17
    P = 0.14P < 0.0001P = 0.73P = 0.009P = 0.006
    Temperaturer = 0.27r = −0.37r = −0.09r = 0.07
    P < 0.0001P < 0.0001P = 0.12P = 0.27
    Humidityr = −0.15r = 0.51r = −0.09
    P = 0.02P < 0.0001P = 0.13
    Pressurer = 0.34r = 0.19
    P < 0.0001P = 0.002
    Humidity changer = −0.22
    P = 0.72

    • Although many variables demonstrated statistically significant correlations, correlation coefficients were generally small. Rather, statistical significance appeared to be driven by our large sample size (n = 244). Therefore, we removed variables with |r| > 0.6 (deleted variables denoted by boldface text). Temperature was strongly correlated with calendar date, so we removed it from our analysis.

  • Table S2.

    Correlation matrix among weather variables used in CART analysis of arrival status at the YP

    VariableWind profitTemperatureHumidityPressureHumidity changePressure change
    Calendar dayr = −0.04r = −0.83r = −0.29r = 0.13r = 0.017r = −0.12
    P = 0.68P < 0.0001P = 0.005P = 0.16P = 0.87P = 0.27
    Wind profitr = −0.05r = −0.32r = 0.16r = 0.05r = −0.24
    P = 0.66P = 0.002P = 0.13P = 0.62P = 0.02
    Temperaturer = 0.24r = −0.40r = −0.26r = 0.14
    P = 0.02P = 0.0001P = 0.01P = 0.18
    Humidityr = 0.10r = 0.56r = 0.16
    P = 0.32P < 0.0001P = 0.13
    Pressurer = 0.39r = 0.12
    P = 0.0001P = 0.27
    Humidity changer = −0.19
    P = 0.07

    • Similar to our departure weather data, many variables demonstrated statistically significant correlations, but correlation coefficients were generally small. Rather, statistical significance appeared to be driven by our large sample size (n = 90). Therefore, we removed variables with |r| > 0.6 (deleted variables denoted by boldface text). Temperature was strongly correlated with calendar date at the time of departure, so we removed it from our CART assessing arrival status.

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Songbirds negotiate the Gulf of Mexico
Jill L. Deppe, Michael P. Ward, Rachel T. Bolus, Robert H. Diehl, Antonio Celis-Murillo, Theodore J. Zenzal, Frank R. Moore, Thomas J. Benson, Jaclyn A. Smolinsky, Lynn N. Schofield, David A. Enstrom, Eben H. Paxton, Gil Bohrer, Tara A. Beveroth, Arlo Raim, Renee L. Obringer, David Delaney, William W. Cochran
Proceedings of the National Academy of Sciences Nov 2015, 112 (46) E6331-E6338; DOI: 10.1073/pnas.1503381112
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