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The effect of call libraries and acoustic filters on the identification of bat echolocation.

Clement MJ, Murray KL, Solick DI, Gruver JC - Ecol Evol (2014)

Bottom Line: We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%).Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species.Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library.

View Article: PubMed Central - PubMed

Affiliation: United States Geological Survey, Patuxent Wildlife Research Center Laurel, Maryland, 20708.

ABSTRACT
Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys.

No MeSH data available.


Related in: MedlinePlus

Schematic of a bat echolocation pulse and relevant parameters. Dur = pulse duration (ms), Sweep = total pulse bandwidth (kHz), TB = duration (s) of the body (the flattest portion of the call), FB = bandwidth (octaves) of the body, Sc = slope of the body (FB/TB; octaves/s), Fc = frequency at the end of the body (kHz), and Tail = duration (ms) of the pulse after the body.
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fig01: Schematic of a bat echolocation pulse and relevant parameters. Dur = pulse duration (ms), Sweep = total pulse bandwidth (kHz), TB = duration (s) of the body (the flattest portion of the call), FB = bandwidth (octaves) of the body, Sc = slope of the body (FB/TB; octaves/s), Fc = frequency at the end of the body (kHz), and Tail = duration (ms) of the pulse after the body.

Mentions: To facilitate comparison of the filters, we included the same covariates in all four DFAs. We included all uncorrelated (Pearson R2 < 0.5) pulse parameters in the models as covariates. When two parameters were correlated, we selected the one that was correlated with fewer parameters, or has been reported as more reliable or diagnostic (Corben 2004; Britzke et al. 2011). We considered 15 pulse parameters provided by AnalookW and two derived parameters. The five selected parameters included pulse duration (Dur), frequency at the end of the body (Fc), slope of the body (Sc), total pulse bandwidth (Sweep), and duration of the pulse after the body (Tail) where the body is defined as the flattest portion of the pulse (Fig.1). Note that although each DFA used the same covariates, each DFA analyzed different data because the data sets were generated by different filters.


The effect of call libraries and acoustic filters on the identification of bat echolocation.

Clement MJ, Murray KL, Solick DI, Gruver JC - Ecol Evol (2014)

Schematic of a bat echolocation pulse and relevant parameters. Dur = pulse duration (ms), Sweep = total pulse bandwidth (kHz), TB = duration (s) of the body (the flattest portion of the call), FB = bandwidth (octaves) of the body, Sc = slope of the body (FB/TB; octaves/s), Fc = frequency at the end of the body (kHz), and Tail = duration (ms) of the pulse after the body.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4228621&req=5

fig01: Schematic of a bat echolocation pulse and relevant parameters. Dur = pulse duration (ms), Sweep = total pulse bandwidth (kHz), TB = duration (s) of the body (the flattest portion of the call), FB = bandwidth (octaves) of the body, Sc = slope of the body (FB/TB; octaves/s), Fc = frequency at the end of the body (kHz), and Tail = duration (ms) of the pulse after the body.
Mentions: To facilitate comparison of the filters, we included the same covariates in all four DFAs. We included all uncorrelated (Pearson R2 < 0.5) pulse parameters in the models as covariates. When two parameters were correlated, we selected the one that was correlated with fewer parameters, or has been reported as more reliable or diagnostic (Corben 2004; Britzke et al. 2011). We considered 15 pulse parameters provided by AnalookW and two derived parameters. The five selected parameters included pulse duration (Dur), frequency at the end of the body (Fc), slope of the body (Sc), total pulse bandwidth (Sweep), and duration of the pulse after the body (Tail) where the body is defined as the flattest portion of the pulse (Fig.1). Note that although each DFA used the same covariates, each DFA analyzed different data because the data sets were generated by different filters.

Bottom Line: We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%).Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species.Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library.

View Article: PubMed Central - PubMed

Affiliation: United States Geological Survey, Patuxent Wildlife Research Center Laurel, Maryland, 20708.

ABSTRACT
Quantitative methods for species identification are commonly used in acoustic surveys for animals. While various identification models have been studied extensively, there has been little study of methods for selecting calls prior to modeling or methods for validating results after modeling. We obtained two call libraries with a combined 1556 pulse sequences from 11 North American bat species. We used four acoustic filters to automatically select and quantify bat calls from the combined library. For each filter, we trained a species identification model (a quadratic discriminant function analysis) and compared the classification ability of the models. In a separate analysis, we trained a classification model using just one call library. We then compared a conventional model assessment that used the training library against an alternative approach that used the second library. We found that filters differed in the share of known pulse sequences that were selected (68 to 96%), the share of non-bat noises that were excluded (37 to 100%), their measurement of various pulse parameters, and their overall correct classification rate (41% to 85%). Although the top two filters did not differ significantly in overall correct classification rate (85% and 83%), rates differed significantly for some bat species. In our assessment of call libraries, overall correct classification rates were significantly lower (15% to 23% lower) when tested on the second call library instead of the training library. Well-designed filters obviated the need for subjective and time-consuming manual selection of pulses. Accordingly, researchers should carefully design and test filters and include adequate descriptions in publications. Our results also indicate that it may not be possible to extend inferences about model accuracy beyond the training library. If so, the accuracy of acoustic-only surveys may be lower than commonly reported, which could affect ecological understanding or management decisions based on acoustic surveys.

No MeSH data available.


Related in: MedlinePlus