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Acoustic identification of individuals within large avian populations: a case study of the Brownish-flanked Bush Warbler, South-Central China.

Xia C, Lin X, Liu W, Lloyd H, Zhang Y - PLoS ONE (2012)

Bottom Line: Most spectro-temporal variables we measured show greater variation among individuals than within individual.We also found that using a part of randomly selected measured variables was sufficient to obtain a high correct rate of individual identification.We believe that our work will increase confidence in the use of using acoustic recognition techniques for avian population monitoring programs.

View Article: PubMed Central - PubMed

Affiliation: Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China.

ABSTRACT
Acoustic identification is increasingly being used as a non-invasive method for identifying individuals within avian populations. However, most previous studies have utilized small samples of individuals (<30). The feasibility of using acoustic identification of individuals in larger avian populations has never been seriously tested. In this paper, we assess the feasibility of using distinct acoustic signals to identify individuals in a large avian population (139 colour-banded individuals) of Brownish-flanked Bush Warbler (Cettia fortipes) in the Dongzhai National Nature Reserve, south-central China. Most spectro-temporal variables we measured show greater variation among individuals than within individual. Although there was slight decline in the correct rate of individual identification with increasing sample sizes, the total mean correct rate yielded by discriminant function analysis was satisfactory, with more than 98% of songs correctly recognized to the corresponding individuals. We also found that using a part of randomly selected measured variables was sufficient to obtain a high correct rate of individual identification. We believe that our work will increase confidence in the use of using acoustic recognition techniques for avian population monitoring programs.

Show MeSH
The correct rate of acoustic identification with different sample sizes for alpha (A) and beta(B) song type.
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pone-0042528-g001: The correct rate of acoustic identification with different sample sizes for alpha (A) and beta(B) song type.

Mentions: DFA yield a high correct rate of acoustic individual identification of individuals although there was a slight decline in the percentage correct rate of individual identification with increasing sample sizes for both alpha and beta song types (Fig. 1). For all the 139 individuals sampled, most songs were correctly classified regardless of whether alpha or beta song types were used. Of the alpha song types, 98.4% of all songs were correctly classified. Songs from 140 variations were 100% correctly classified, songs from 18 variations were 80–90% correctly classified, and only one variation was 50% correctly classified. For beta song type, 98.8% of all songs were correctly classified. The songs from 117 variations were 100% correctly classified, while songs from other 10 variations were 70%–90% correctly classified.


Acoustic identification of individuals within large avian populations: a case study of the Brownish-flanked Bush Warbler, South-Central China.

Xia C, Lin X, Liu W, Lloyd H, Zhang Y - PLoS ONE (2012)

The correct rate of acoustic identification with different sample sizes for alpha (A) and beta(B) song type.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3412828&req=5

pone-0042528-g001: The correct rate of acoustic identification with different sample sizes for alpha (A) and beta(B) song type.
Mentions: DFA yield a high correct rate of acoustic individual identification of individuals although there was a slight decline in the percentage correct rate of individual identification with increasing sample sizes for both alpha and beta song types (Fig. 1). For all the 139 individuals sampled, most songs were correctly classified regardless of whether alpha or beta song types were used. Of the alpha song types, 98.4% of all songs were correctly classified. Songs from 140 variations were 100% correctly classified, songs from 18 variations were 80–90% correctly classified, and only one variation was 50% correctly classified. For beta song type, 98.8% of all songs were correctly classified. The songs from 117 variations were 100% correctly classified, while songs from other 10 variations were 70%–90% correctly classified.

Bottom Line: Most spectro-temporal variables we measured show greater variation among individuals than within individual.We also found that using a part of randomly selected measured variables was sufficient to obtain a high correct rate of individual identification.We believe that our work will increase confidence in the use of using acoustic recognition techniques for avian population monitoring programs.

View Article: PubMed Central - PubMed

Affiliation: Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, China.

ABSTRACT
Acoustic identification is increasingly being used as a non-invasive method for identifying individuals within avian populations. However, most previous studies have utilized small samples of individuals (<30). The feasibility of using acoustic identification of individuals in larger avian populations has never been seriously tested. In this paper, we assess the feasibility of using distinct acoustic signals to identify individuals in a large avian population (139 colour-banded individuals) of Brownish-flanked Bush Warbler (Cettia fortipes) in the Dongzhai National Nature Reserve, south-central China. Most spectro-temporal variables we measured show greater variation among individuals than within individual. Although there was slight decline in the correct rate of individual identification with increasing sample sizes, the total mean correct rate yielded by discriminant function analysis was satisfactory, with more than 98% of songs correctly recognized to the corresponding individuals. We also found that using a part of randomly selected measured variables was sufficient to obtain a high correct rate of individual identification. We believe that our work will increase confidence in the use of using acoustic recognition techniques for avian population monitoring programs.

Show MeSH