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Differential geographic patterns in song components of male Albert’s lyrebirds

Citation

Backhouse, Fiona et al. (2022), Differential geographic patterns in song components of male Albert’s lyrebirds, Dryad, Dataset, https://doi.org/10.5061/dryad.w3r2280pn

Abstract

Geographic variation in bird song has received much attention in evolutionary studies, yet few consider components within songs that may be subject to different constraints and follow different evolutionary trajectories. Here we quantify patterns of geographic variation in the socially-transmitted ‘whistle’ song of Albert’s lyrebirds (Menura alberti), an oscine passerine renowned for its remarkable vocal abilities. Albert’s lyrebirds are confined to narrow stretches of suitable habitat, allowing us to map likely paths of cultural transmission using a species distribution model and least cost paths. We use quantitative methods to break the songs into three components present in all study populations: the introductory elements, the song body, and the final element. We compare geographic separation between populations with variation in these components as well as the full song. All populations were distinguishable by song, and songs varied according to the geographic distance between populations. However, within songs, only the introductory elements and song body could be used to distinguish among populations. The song body and final element changed with distance, but the introductory elements varied independently of geographic separation. These differing geographic patterns of within-song variation are unexpected, given that the whistle song components are always produced in the same sequence and may be perceived as a temporally-discrete unit. Knowledge of such spatial patterns of within-song variation enables further work to determine possible selective pressures and constraints acting on each song component, and provides spatially-explicit targets for preserving cultural diversity. As such, our study highlights the importance for science and conservation management of investigating spatial patterns within seemingly discrete behavioural traits at multiple levels of organisation.

Methods

See paper (open access).

Usage Notes

Description of each file:

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ALB.elements.csv

Measurements of all elements within all songs used in the analysis. This dataset was used in the PCA on all elements (Fig. 2) and to create all other song datasets.

Description of columns in ALB.elements.csv

Name of Column

Description

View

Specifies measurement was taken from the spectrogram

Begin.Time

Begin time of the element within the recording

End.Time

End time of the element within the recording

Low.Freq

The lowest frequency of the element

High.Freq

The highest frequency of the element

BW.90

Frequency bandwidth within which 90% of the power within the element occurs

Peak.Freq

Frequency with the highest power in the element

Centre.Freq

Frequency that divides the element into two frequency intervals of equal energy

Begin.File

Name of the recording file the element is in

Location

Study site the recording of the element was taken from

Bird.ID

Identity of bird recorded

Dur.90

Duration within which 90% of the power within the element occurs

Peak.Freq.Contour

Vector of the peak frequencies of each spectrogram slice in the selection

Freq.5

Frequency at which 5% of the energy in the element is below

Freq.95

Frequency at which 95% of the energy in the element is below

Type

Broad categorical description of the element shape/quality

Song

Song the element is from. Unique within each recording file

Order.pct

The order at which the element occurs in the song, calculated as a percentage of the number of elements in the song.

Begin.freq

The first frequency in the peak frequency contour

End.freq

The last frequency in the peak frequency contour

Slope

The difference between End.freq and Begin.freq

Type.cat

The type of element within the song - introductory, body, or final

Type.b

The type of element within the song, distinguishing the "buzz" final elements of Goomburra from other final elements 

PC1

The first principal component as calculated in Minitab

PC2

The second principal component as calculated in Minitab

PC3

The third principal component as calculated in Minitab

~~~~~

intro.elements.csv

Contains the data required for the analysis on the introductory elements.

Description of columns in intro.elements.csv

Name of Column

Description

Begin.File

Name of the recording file the element is in

Location

Study site the recording was taken in

Bird.ID

Identity of the bird recorded

Song

Song within the recording the element is from

BW.90

Frequency bandwidth within which 90% of the power within the element occurs

Peak.Freq

Frequency with the highest power in the element

Dur.90

Duration within which 90% of the power within the element occurs

Begin.freq

The first frequency in the peak frequency contour

End.freq

The last frequency in the peak frequency contour

log.dur

log transformation of 90% duration

PC1

The first principal component as calculated in Minitab

PC2

The second principal component as calculated in Minitab

PC3

The third principal component as calculated in Minitab

PC4

The fourth principal component as calculated in Minitab

PC5

The fifth principal component as calculated in Minitab

~~~~~

final.elements.nobuzz.csv

Contains the data required for the analysis on final elements. Note that this does not include the "buzz" elements from Goomburra. A dataset with all final elements including the "buzz" elements derived from "ALB.elements.csv" is included as "final.elements.csv".

Description of columns in final.elements.nobuzz.csv

Name of Column

Description

Begin.File

Name of the recording file the element is in

Location

Study site the recording was taken in

Bird.ID

Identity of the bird recorded

Song

Song within the recording the element is from

BW.90

Frequency bandwidth within which 90% of the power within the element occurs

Peak.Freq

Frequency with the highest power in the element

Dur.90

Duration within which 90% of the power within the element occurs

Begin.freq

The first frequency in the peak frequency contour

End.freq

The last frequency in the peak frequency contour

log.endfr

log transformation of 4000 minus End.freq

PC1

The first principal component as calculated in Minitab

PC2

The second principal component as calculated in Minitab

PC3

The third principal component as calculated in Minitab

PC4

The fourth principal component as calculated in Minitab

PC5

The fifth principal component as calculated in Minitab

~~~~~

song.body.csv

ALB.songs.csv

Contains the data required for the analysis on the song body and the full song respectively. Column names and descriptions are identical in each dataset.

Description of columns in song.body.csv and ALB.songs.csv

Column Name

Description

Location

Study site the recording was taken in

Bird.ID

Identity of the bird recorded

Begin.File

Name of the recording file the element is in

Song

Song identity within each recording

Length

Song duration

Freq.range.90

90% song bandwidth

Max.freq

Peak frequency of the higest element in the song

Min.freq

Peak frequency of the lowest element in the song

CV.pf

Coefficient of variation of element peak frequency

CV.dur

Coefficient of variation of 90% element duration

slope

Song slope as a regression of centre frequency over the position of the element within the song (order.pct)

PC1

The first principal component as calculated in Minitab

PC2

The second principal component as calculated in Minitab

PC3

The thiird principal component as calculated in Minitab

PC4

The fourth principal component as calculated in Minitab

PC5

The fifth principal component as calculated in Minitab

~~~~~

distances.csv

Contains the geographic distance measures between each population, required for the mantel tests. Can be converted into distance matrices.

Description of columns in distances.csv

Column name

Description

Site.x

First location in the distance measure. BB = Lamington (Binna Burra), BR = Border Ranges, GB = Goomburra, KL = Killarney, MJ = Mt Jerusalem, TM = Tamborine

Site.y

The second location in the distance measure

PathCost

Cost of the least cost path between locations

Length

Length of the least cost path between locations

SL.dist

Straight line (geodetic) distance between locations

resist.length

Cost x length of the least cost path between locations, used in paper as measure of geographic separation


~~~~~

ALB_Whistle_song_analysis.R

R code used to create the song and song component datasets, and for each analysis. R code is annotated.

~~~~~

SDM_layer.zip

The Species Distribution Model layer, used to calculate least cost paths between locations. The layer file is called "species.asc" and can be opened in ArcMap.

Funding

National Science Foundation, Award: 1730791

BirdLife Northern NSW*

BirdLife Northern NSW