In late April 2022, during the hearing of the audio files from an unsupervised bioacoustic assessment of the shearwater populations (Aves, Procellariiformes) on the coast of Pantelleria island (Italy, Sicily), a cricket song of unknown attribution was heard. The first bioacoustic analyses, including FFT-based spectrograms and sound pressure envelopes, confirmed that it could not be attributed to the known sound of any Italian nor Mediterranean species of cricket. In the ensuing weeks, field research made on purpose in the original station and in further localities in the southern coast of Pantelleria provided photographs, living specimens and further audio records. As soon as the photos were shared among the authors, it became clear that the species belonged to the genus Acheta. Further bioacoustic analyses and morphological comparison with type specimens of Mediterranean and North-African congenerics in relevant collections and in the scientific literature were conducted: they confirmed that the findings could only be attributed to a still undescribed species, that escaped detection due to its impervious and unfrequented habitat. Acheta pantescus n. sp. is apparently restricted to the effusive coastal cliffs of the island of Pantelleria, a habitat whose scant extension and vulnerability require environmental protection actions such as the inclusion in a special Red List by the IUCN Italian Committee.

The automatic classification of bird sounds is an ongoing research topic and several results have been reported for the classification of selected bird species. In this contribution we use an artificial neural network fed with pre-computed sound features to study the robustness of bird sound classification. We investigate in detail if and how classification results are dependent on the number of species and the selection of species in the subsets presented to the classifier. In more detail, a bag-of-birds approach is employed to randomly create balanced subsets of sounds from different species for repeated classification runs. The number of species present in each subset is varied between 10 and 300, randomly drawing sounds of species from a dataset of 659 bird species taken from Xeno-Canto database. We observe that the shallow artificial neural network trained on pre-computed sound features is able to classify the bird sounds relatively well. The classification performance is evaluated using several common measures such as precision, recall, accuracy, mean average precision and area under the receiver operator characteristics curve. All of these measures indicate a decrease in classification success as the number of species present in the subsets is increased. We analyze this dependence in detail and compare the computed results to an analytic explanation assuming dependencies for an idealized perfect classifier. Moreover, we observe that the classification performance depends on the individual composition of the subset and varies across 20 randomly drawn subsets.

Globally, anthropogenic sound and artificial light pollution have increased to alarming levels. Evidence suggests that these can disrupt critical processes that impact ecosystems and human health. However, limited focus has been given to the potential effects of sound and artificial light pollution on microbiomes. Microbial communities are the foundations of our ecosystems. They are essential for human health and provide myriad ecosystem services. Therefore, disruption to microbiomes by anthropogenic sound and artificial light could have important ecological and human health implications. In this mini-review, we provide a critical appraisal of available scientific literature on the effects of anthropogenic sound and light exposure on microorganisms and discuss the potential ecological and human health implications. Our mini-review shows that a limited number of studies have been carried out to investigate the effects of anthropogenic sound and light pollution on microbiomes. However, based on these studies, it is evident that anthropogenic sound and light pollution have the potential to significantly influence ecosystems and human health via microbial interactions. Many of the studies suffered from modest sample sizes, suboptimal experiments designs, and some of the bioinformatics approaches used are now outdated. These factors should be improved in future studies. This is an emerging and severely underexplored area of research that could have important implications for global ecosystems and public health. Finally, we also propose the photo-sonic restoration hypothesis: does restoring natural levels of light and sound help to restore microbiomes and ecosystem stability?