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Bats have an important role in the ecosystem, and therefore an effective detection of their prevalence can contribute to their conservation. At present, the most commonly methodology used in the study of bats is the analysis of echolocation calls. However, many other ultrasound signals can be simultaneously recorded, and this makes species location and identification a long and difficult task. This field of research could be greatly improved through the use of bioacoustics which provide a more accurate automated detection, identification and count of the wildlife of a particular area. We have analyzed the calls of two bat species—Pipistrellus pipistrellus and Pipistrellus pygmaeus—both of which are common types of bats frequently found in the Iberian Peninsula. These two cryptic species are difficult to identify by their morphological features, but are more easily identified by their echolocation calls. The real-life audio files have been obtained by an Echo Meter Touch Pro 1 bat detector. Time-expanded recordings of calls were first classified manually by means of their frequency, duration and interpulse interval. In this paper, we first detail the creation of a dataset with three classes, which are the two bat species but also the silent intervals. This dataset can be useful to work in mixed species environment. Afterwards, two automatic bat detection and identification machine learning approaches are described, in a laboratory environment, which represent the previous step to real-life in an urban scenario. The priority in that approaches design is the identification using short window analysis in order to detect each bat pulse. However, given that we are concerned with the risks of automatic identification, the main aim of the project is to accelerate the manual ID process for the specialists in the field. The dataset provided will help researchers develop automatic recognition systems for a more accurate identification of the bat species in a laboratory environment, and in a near future, in an urban environment, where those two bat species are common.

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There is a lack of studies designed to detect the most important areas for bat conservation. In this context, areas of high bat activity have been rarely considered in the delimitation of protected areas for bats, which are generally focused on the protection of roosting sites. This has been due to the difficulties of sampling the distribution of these nocturnal animals when moving at night. This methodological constraint has been overcome by the development of bioacoustic sampling, which allows mapping the occurrence of active bats over large areas. In this study, we use bat detectors to sample the distribution of bat activity in central Spain. This region is under the environmental effects of a mountain range (Guadarrama Mountains) and the urban encroachment of the city of Madrid. The occurrences provided by the detectors were used to produce species distribution models of which the resulting layers were arranged to detect the most suitable areas for bat richness and rarity indices. We performed a gap analysis to explore whether the areas most commonly used by active bats are covered by the current network of protected areas. The results showed that the best areas of high bat activity are located at the piedmont of the mountains and that most of these areas overlap with the existing network of protected areas. The best areas for bats excluded the most urbanized areas and within a similar urban gradient, protected areas tended to be located within the best sites for conservation. These results suggest that bats currently benefit from a network of protected areas initially aimed to protect birds and habitats (Natura 2000). In addition, monitoring areas of high bat activity could complement roosting site protection in the conservation of bat assemblages.

There has been an increase in commercial bat detectors and noise filtering software for monitoring bat activity. In this study, we compare the recording efficiency of three bat detectors from the popular brand Wildlife Acoustics (Echo Meter 3, Echo Meter Touch Pro 1 and Song Meter 2 BAT) and the effectiveness of two noise filtering software (Kaleidoscope and SonoBat Batch Scrubber). To do so, we recorded 7513 files from 13 urban parks in Madrid in 2017, that were manually identified to species level. The results show that the Echo Meter 3 records significantly less activity than the Echo Meter Touch Pro 1 and Song Meter 2 BAT. Our results also identify SonoBat Batch Scrubber as more reliable than Kaleidoscope for preventing false negatives. Therefore, our study demonstrates that different bat detectors, and different noise filtering software, can provide different results.