The assessment of biases in the acoustic discrimination of individuals

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.

Measuring individual identity information in animal signals: Overview and performance of available identity metrics

“…While the general performance of identity metrics was evaluated on simulated datasets, empirical datasets were used to evaluate the consistency of DS and H S metrics and reliability of H S and DS conversion on real data. We used six empirical datasets from four different species: little owls Athene noctua (ANmodulation, ANspec) (Linhart & Šálek, 2017), corncrake Crex crex (CCformants, CCspec) (Budka & Osiejuk, 2013), yellow-breasted boubous Laniarius atroflavus F I G U R E 1 Illustration of three artificial multivariate datasets that differ only in the individuality used to generate datasets. Settings for the function generating these datasets: i = 5, o = 10, p = 2, cov = 0, id = 0.01, 3 and 10 In two species -corncrakes and little owls -calls were described by two different sets of variables.…”

Measuring individual identity information in animal signals: Overview and performance of available identity metrics

“…We used six datasets from four different species: little owls Athene noctua (ANmodulation, ANspec) (Linhart & Šálek, 2017), corncrake Crex crex (CCformants, CCspec) (Budka & Osiejuk, 2013), yellow-breasted boubous Laniarius atroflavus (LAhighweewoo) (Osiejuk et al unpublished data), and domestic pigs Sus scrofa (SSgrunts) (Syrová, Policht, Linhart, & Špinka, 2017) (Figure 2). In two species – corncrakes and little owls – calls were described by two different sets of variables.…”

“…Total sample size (H Sntot ), number of groups (i.e., individuals) (H Sngroups ), and number of samples per group (H Snpergroup ) could all be used as ‘n’ in this equation. Some studies explicitly state they used number of individuals as ‘n’ (e.g., Pollard, Blumstein, & Griffin, 2010; Linhart & Šálek, 2017), but the properties of H S values in these studies did not match the properties suggested in the original article by Beecher (1989). Yet another approach to calculate H S is to extract the variance component estimates and use the total (⍰ T ) and the residual variance (⍰ W , associated with random factor) to calculate H S (H Svarcomp ) (Beecher, 1989; Carter, Logsdon, Arnold, Menchaca, & Medellin, 2012): …”

“…A variety of identity metrics have proliferated because the existing metrics were considered biased (Beecher, 1989; Mathevon et al, 2010) or unsuitable for a particular signal type (Searby & Jouventin, 2004). Furthermore, different equations have been sometimes used to calculate the same identity metric (Beecher, 1989; Lein, 2008; Charrier, Aubin, & Mathevon, 2010; Linhart & Šálek, 2017). Thus there is no consensus about how to properly measure identity.…”

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

“…Beyond supporting established survey approaches, PAM also offers unique possibilities, including study of vocalising behaviour, intraspecific variability in call repertoire, and the evolution of acoustic communities (Blumstein et al, 2011;Linhart & Šálek, 2017;Prat, Taub, & Yovel, 2016;Tobias, Planqué, Cram, & Seddon, 2014); animal responses to the acoustic environment (Nowacek et al, 2016;Simpson, Meekan, Jeffs, Montgomery, & McCauley, 2008); and monitoring of anthropogenic phenomena such as sound pollution, blast fishing, and poaching (Astaras, Linder, Wrege, Orume, & Macdonald, 2017;Braulik et al, 2017) ( Table 1). There is a rich literature on the effects of anthropogenic noise on cetacean and increasingly avian populations and behaviour (e.g., Pirotta, Merchant, Thompson, Barton, & Lusseau, 2015;Proppe, Sturdy, & St. Clair, 2013).…”