Data, Sample Sizes and Statistics Affect the Recognition of Species of Bats by Their Echolocation Calls

Biscardi, Stefania; Orprecio, Jazmine; Fenton, M. Brock; Tsoar, Asaf; Ratcliffe, John M.

doi:10.3161/001.006.0212

Cited by 33 publications

(15 citation statements)

References 43 publications

(79 reference statements)

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“…alecto , a species that to our knowledge has not been described before. Furthermore, our data corroborate the general findings of other studies on search calls of neotropical emballonurid bats (Barclay, 1983; Kalko, 1995; O'Farrell & Miller, 1997; Fenton et al , 1999; O'Farrell et al , 1999; Ochoa et al , 2000; Ibáñez et al , 2002; Rydell et al , 2002; Bayefsky‐Anand, 2006) except for Biscardi et al (2004) who recorded P. macrotis calling at 42 kHz in Brazil, which is at least 3 kHz higher than our recordings and those of Rydell et al (2002) from Central America. This may hint towards geographic variation, differences in body size or the involvement of two species instead of one.…”

Section: Discussionmentioning

confidence: 94%

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Echolocation calls in Central American emballonurid bats: signal design and call frequency alternation

2007

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In southern Central America, 10 species of emballonurid bats occur, which are all aerial insectivores: some hunt flying insects preferably away from vegetation in open space, others hunt in edge space near vegetation and one species forages mainly over water. We present a search call design of each species and link signal structure to foraging habitat. All emballonurid bats use a similar type of echolocation call that consists of a central, narrowband component and one or two short, frequency-modulated sweeps. All calls are multi-harmonic, generally with most energy concentrated in the second harmonic. The design of search calls is closely related to habitat type, in particular to distance of clutter. Emballonurid bats foraging in edge space near vegetation and over water used higher frequencies, shorter call durations and shorter pulse intervals compared with species mostly hunting in open, uncluttered habitats. Peak frequency correlated negatively with body size. Regular frequency alternation between subsequent calls was typical in the search sequences of four out of 10 species. We discuss several hypotheses regarding the possible role of this frequency alternation, including species identification and partitioning of acoustic channels. Furthermore, we propose a model of how frequency alternation could increase the maximum detection distance of obstacles by marking search calls with different frequencies.

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Section: Discussionmentioning

confidence: 94%

“…Interestingly, S. canescens does not alter call frequency in contrast to its congeners S. bilineata and S. leptura . A further, up to now unidentified emballonurid species with a peak frequency of about 59 kHz was recorded in Para, Brazil by Biscardi et al (2004).…”

Section: Discussionmentioning

confidence: 99%

Echolocation calls in Central American emballonurid bats: signal design and call frequency alternation

2007

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“…We used discriminant function analysis (DFA) for our classification model because it is most commonly used to quantitatively identify bat calls (Biscardi et al. ), although other statistical methods may be appropriate as well (Preatoni et al. ).…”

Section: Methodsmentioning

confidence: 99%

“…The typical approach to model validation is to divide a call library into training and validation data, using leave‐one‐out cross‐validation, k‐fold partitioning, or a similar approach (e.g., Biscardi et al. ; Berger‐Tal et al. ; Britzke et al.…”

Section: Methodsmentioning

confidence: 99%

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

Clement

Murray

Solick

et al. 2014

Ecology and Evolution

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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.

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“…The most commonly used tools for quantitative acoustic differentiation between species are parametric models such as discriminant function analysis or logistic regression (Biscardi, Orprecio, Fenton, Tsoar, & Ratcliffe, ; Oswald et al, ; Smith, Newman, Hoffman, & Fetterly, ; Steiner, ; Teixeira & Jesus, ; Vaughan, Jones, & Harris, ). Newer nonparametric models such as k ‐nearest neighbor and neural networks have also been successful at species classification (Britzke, Duchamp, Murray, Swihhart, & Robbins, ; Parsons, ; Redgwell, Szewczak, Jones, & Parsons, ).…”

Section: Introductionmentioning

confidence: 99%

Quantitative acoustic differentiation of cryptic species illustrated with King and Clapper rails

Stiffler

Schroeder

Anderson

et al. 2018

Ecology and Evolution

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Reliable species identification is vital for survey and monitoring programs. Recently, the development of digital technology for recording and analyzing vocalizations has assisted in acoustic surveying for cryptic, rare, or elusive species. However, the quantitative tools that exist for species differentiation are still being refined. Using vocalizations recorded in the course of ecological studies of a King Rail (Rallus elegans) and a Clapper Rail (Rallus crepitans) population, we assessed the accuracy and effectiveness of three parametric (logistic regression, discriminant function analysis, quadratic discriminant function analysis) and six nonparametric (support vector machine, CART, Random Forest, k‐nearest neighbor, weighted k‐nearest neighbor, and neural networks) statistical classification methods for differentiating these species by their kek mating call. We identified 480 kek notes of each species and quantitatively characterized them with five standardized acoustic parameters. Overall, nonparametric classification methods outperformed parametric classification methods for species differentiation (nonparametric tools were between 57% and 81% accurate, parametric tools were between 57% and 60% accurate). Of the nine classification methods, Random Forest was the most accurate and precise, resulting in 81.1% correct classification of kek notes to species. This suggests that the mating calls of these sister species are likely difficult for human observers to tell apart. However, it also implies that appropriate statistical tools may allow reasonable species‐level classification accuracy of recorded calls and provide an alternative to species classification where other capture‐ or genotype‐based survey techniques are not possible.

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Data, Sample Sizes and Statistics Affect the Recognition of Species of Bats by Their Echolocation Calls

Cited by 33 publications

References 43 publications

Echolocation calls in Central American emballonurid bats: signal design and call frequency alternation

Echolocation calls in Central American emballonurid bats: signal design and call frequency alternation

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

Quantitative acoustic differentiation of cryptic species illustrated with King and Clapper rails

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