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. 2017 May 9;12(5):e0177206.
doi: 10.1371/journal.pone.0177206. eCollection 2017.

The Assessment of Biases in the Acoustic Discrimination of Individuals

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Free PMC article

The Assessment of Biases in the Acoustic Discrimination of Individuals

Pavel Linhart et al. PLoS One. .
Free PMC article

Erratum in

Abstract

Animal vocalizations contain information about individual identity that could potentially be used for the monitoring of individuals. However, the performance of individual discrimination is subjected to many biases depending on factors such as the amount of identity information, or methods used. These factors need to be taken into account when comparing results of different studies or selecting the most cost-effective solution for a particular species. In this study, we evaluate several biases associated with the discrimination of individuals. On a large sample of little owl male individuals, we assess how discrimination performance changes with methods of call description, an increasing number of individuals, and number of calls per male. Also, we test whether the discrimination performance within the whole population can be reliably estimated from a subsample of individuals in a pre-screening study. Assessment of discrimination performance at the level of the individual and at the level of call led to different conclusions. Hence, studies interested in individual discrimination should optimize methods at the level of individuals. The description of calls by their frequency modulation leads to the best discrimination performance. In agreement with our expectations, discrimination performance decreased with population size. Increasing the number of calls per individual linearly increased the discrimination of individuals (but not the discrimination of calls), likely because it allows distinction between individuals with very similar calls. The available pre-screening index does not allow precise estimation of the population size that could be reliably monitored. Overall, projects applying acoustic monitoring at the individual level in population need to consider limitations regarding the population size that can be reliably monitored and fine-tune their methods according to their needs and limitations.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Illustration of little owl call and three methods used for the call description.
Example the single territorial call of the little owl male (spectrogram and oscilogram, a), and an illustration of the three call description methods: b) description of call spectral features (1 = minF, 2 = q25, 3 = dF, 4 = q50, 5 = q75, 6 = maxF); c) description of call frequency modulation; and d) cross-correlation of calls (rectangles indicate cross-correlating segments between two displayed calls). Spectrogram settings: FFT-length = 512, window type = Flat Top, window overlap = 93.75%.
Fig 2
Fig 2. Relationship between the discrimination performance at the call and at the individual level.
In this hypothetical example, calling bouts of 20 calls each from individuals A, B, and C are attributed to three individuals by linear discriminant analysis (LDA). Rows represent to which individual calls belonged to and collumns represent to which individual calls were assigned to by LDA. Diagonal represents calls that were attributed to correct individuals. There is 100% discrimination success at the individual level because all three call sets were assigned to correct individual based on majority criterion. Even for C, the set of 20 calls would be correctly identified as belonging to individual C as majority of the calls (40%) were assigned to C. On the other hand, discrimination performance at the call level would be only 63% (overall percentage of correctly assigned calls).
Fig 3
Fig 3. Effect of increasing number of individuals on discrimination performance.
Effect of increasing number of individuals on discrimination performance at the level of calls (a) and at the level of individuals (b) for the three call description methods.
Fig 4
Fig 4. Relationship between the HS and population size to be monitored.
(a) HS as a function of the number of individuals in the sample. (b) Relationship between average HS computed from subsample of 10 random individuals HS(10) or full sample of 54 males HS(54). (c) Relationship between HS and call discrimination performance. (d) Relationship between the estimated and real number of discriminated individuals. Grey line illustrates y = x line for ideal estimates. HS in (b), (c), and (d) was computed for 23 discrimination models that differed in how many and which measuring points (F1 –F20) were included (S1 Table).
Fig 5
Fig 5. Effect of number of calls per male available on the discrimination performance.
(a) Changes in performance with increasing number of calls available for discriminant function (for all 54 males). (b) Population size to be monitored if 90% individuals are to be classified correctly. (c) Population size to be monitored if 65% of calls are to be identified correctly.

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Grant support

PL was supported by the Czech Science Foundation (GA14-27925S), Ministry of Agriculture of Czech Republic (MZERO0716), and National Science Centre, Poland (2015/19/P/NZ8/02507); MŠ was funded by the Academy of Sciences of the Czech Republic (RVO68081766). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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