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

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 (R. 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 67–81% accurate, parametric tools were
between 56–60% 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.