Fig 1.
Illustration of the exclusion method.
The x-axis represents the acoustic variable of interest (e.g. peak frequency) with different, but overlapping, distributions in males (blue) and females (red); both represented as a probability density function. Red/blue dashed lines depict the investigated lower/upper boundaries for the exclusion zone, with different stringency thresholds used to define the overlap zone.
Table 1.
Proportion of males and peak frequencies of the studied colonies.
The proportion of males was estimated with the genetic (Gen.), acoustic ABC (ABC), and acoustic 99.9% exclusion methods. The 95% highest density interval (HDI) is presented for parameters estimated via the ABC approach. Peak frequencies were estimated with the acoustic ABC approach.
Fig 2.
Mean peak frequencies of males and females and within-sex standard deviation of peak frequency.
Same sd for both sexes, estimated via ABC across simulations (i.e. including all tested sample sizes: 100, 500, 1000, 2500, 5000, 10000; with 100 simulated data sets for each of the 126 combinations of a given proportion of males and sample size).
Fig 3.
Mean peak frequency of males (top) and females (middle) and the within-sex (equal for males and females) standard deviation of calls (bottom).
Estimated via ABC for the same datasets as in Fig 2, but plotted separately for the six different sample sizes (represented by blocks).
Fig 4.
Proportion of males estimated via the ABC method versus the simulated (true) value.
For 100, 500, 1000, 2500, 5000, and 10000 calls (same datasets as Figs 2 and 3). The dashed line depicts a perfect match between simulated and estimated values.
Fig 5.
Root mean square error (RMSE) for different sex ratios estimated via the exclusion method using thresholds of 95% (blue), 99% (yellow), and 99.9% (green), and via the ABC approach (black).
Blocks correspond to different sample sizes as indicated. The X-axis represents the simulated proportion of males (from 0 to 1 in steps of 0.05, same datasets as Figs 2–4).
Fig 6.
Comparison of RMSEs obtained with the one-step versus the two-step ABC method.
Table 2.
Identification of the caller’s sex from simulated datasets.
The difference between male and female simulated mean peak frequencies (Δ) was between 1 and 5 kHz. The number of calls per dataset ranged from N = 25 to N = 1000. Values presented correspond to the percentage (mean and sd) of individuals assigned to the correct sex out of 9,000 simulations (1000 simulations per considered proportion of males going from 0.1 to 0.9 in steps of 0.1). The percentage of calls within the overlap zone between sexes is also presented as a measure of overlap (mean and sd).
Fig 7.
Percentage of calls correctly identified as male (yellow) or female (blue).
In this example, simulated mean peak frequencies of both sexes differed by 2 kHz and the number of calls per dataset ranged from N = 25 to N = 1000. Values are presented for 1000 simulated datasets per considered proportion of males (0.1 to 0.9 in steps of 0.1) and number of calls.
Fig 8.
Influence of the proportion of males on the global percentage of correct sex assignment of calls when male and female mean peak frequencies differ by 1–5 kHz.
Values for different sample sizes (cf. Fig 7) and sexes are combined.
Fig 9.
Impact of the accuracy of the assumed mean peak frequencies on the performance of the exclusion method (99.9% threshold).
The x-axis shows the difference between the assumed and true mean peak frequency of male calls. Green lines represent an overestimation of the proportion of males, blue lines an underestimation. The dashed, broken, and dotted lines depict sex ratios estimated for different assumed male mean peak frequencies when the simulated POM is 0, 0.5, or 1, respectively.