Variability in echolocation call design of 26 Swiss bat species: consequences, limits and options for automated field identification with a synergetic pattern recognition approach

TLDR

Pattern recognition algorithms, including synergetic classifiers, hold promise for operator‑independent bat species identification from echolocation calls, but assembling a suitable reference call database is a non‑trivial challenge. The study aims to examine how species‑specific echolocation call variability affects the design of an automated, species‑specific bat activity monitoring system. The authors provide call‑parameter flexibility data for 26 Swiss bat species and analyze how training‑call selection influences classifier performance.

Abstract

Pattern recognition algorithms offer a promising approach to recognizing bat species by their echolocation calls. Automated systems like synergetic classifiers may contribute significantly to operator-independent species identification in the field. However, it necessitates the assembling of an appropriate database of reference calls, a task far from trivial. We present data on species specific flexibility in call parameters of all Swiss bat species (except Nyctalus lasiopterus and Plecotus alpinus ). The selection of "training-calls" for the classifier is crucial for species identification success. We discuss this in the context of echolocation call variability differing between species and its consequences for the implementation of an automated, species specific bat activity monitoring system.