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Animal acoustic signals are widely used in diverse research areas due to the relative ease with which sounds can be registered across a wide range of taxonomic groups and research settings. However, bioacoustics research can quickly generate large data sets, which might prove challenging to analyze promptly. Although many tools are available for the automated detection of sounds, choosing the right approach can be difficult only a few tools provide a framework for evaluating detection performance. Here we presentohun, an R package intended to facilitate automated sound detection.ohunprovides functions to diagnose and optimize detection routines and compare performance among different detection approaches. The package uses reference annotations containing the time position of target sounds in a training data set to evaluate detection routines performance using common signal detection theory indices. This can be done both with routine outputs imported from other software and detections run within the package. The package also provides functions to organize acoustic data sets in a format amenable to detection analyses. ohun also includes energy-based and template-based detection methods, two commonly used automatic approaches in bioacoustic research. We show how ohun automatically can be used to detect vocal signals with case studies of adult male zebra finch (Taenopygia gutata) songs and Spix’s disc-winged bat (Thyroptera tricolor) ultrasonic social calls. We also include examples of how to evaluate the detection performance of ohun and external software. Finally, we provide some general suggestions to improve detection performance.

Roosts are vital for the survival of many species, and how individuals choose one site over another is affected by various factors. In bats, for example, species may use stiff roosts such as caves or compliant ones such as leaves; each type requires not only specific morphological adaptations but also different landing manoeuvres. Selecting a suitable roost within those broad categories may increase landing performance, reducing accidents and decreasing exposure time to predators. We addressed whether bats select specific roost sites based on the availability of a suitable landing surface, which could increase landing performance. Our study focused on Spix's disc-winged bats (Thyroptera tricolor), a species known to roost within developing tubular leaves. As previous studies show that this species relies on the leaves' apex for safe landing and rapid post-landing settlement, we predicted that bats would prefer to roost in tubular structures with a longer apex and that landing would be consistently more effective on those leaves. Field observations showed that T. tricolor predominantly used two species for roosting, Heliconia imbricata and Calathea lutea, but they preferred roosting in the former. The main difference between these two plant species was the length of the leaf's apex (longer in H. imbricata). Experiments in a flight cage also showed that bats used more consistent approach and landing tactics when accessing leaves with a longer apex. Our results suggest that landing mechanics may strongly influence resource selection, especially when complex manoeuvres are needed to acquire those resources.