Listening carefully: increased perceptual acuity for species discrimination in multispecies signalling assemblages

Communication is a fundamental component of evolutionary change because of its role in mate choice and sexual selection. Acoustic signals are a vital element of animal communication and sympatric species may use private frequency bands to facilitate intraspecific communication and identification of conspecifics (acoustic communication hypothesis, ACH). If so, animals should show increasing rates of misclassification with increasing overlap in frequency between their own calls and those used by sympatric heterospecifics. We tested this on the echolocation of the horseshoe bat, Rhinolophus capensis, using a classical habituation-dishabituation experiment in which we exposed R. capensis from two phonetic populations to echolocation calls of sympatric and allopatric horseshoe bat species (Rhinolophus clivosus and Rhinolophus damarensis) and different phonetic populations of R. capensis. As predicted by the ACH, R. capensis from both test populations were able to discriminate between their own calls and calls of the respective sympatric horseshoe bat species. However, only bats from one test population were able to discriminate between calls of allopatric heterospecifics and their own population when both were using the same frequency. The local acoustic signalling assemblages (ensemble of signals from sympatric conspecifics and heterospecifics) of the two populations differed in complexity as a result of contact with other phonetic populations and sympatric heterospecifics. We therefore propose that a hierarchy of discrimination ability has evolved within the same species. Frequency alone may be sufficient to assess species membership in relatively simple acoustic assemblages but the ability to use additional acoustic cues may have evolved in more complex acoustic assemblages to circumvent misidentifications as a result of the use of overlapping signals. When the acoustic signal design is under strong constraints as a result of dual functions and the available acoustic space is limited because of co-occurring species, species discrimination is mediated through improved sensory acuity in the receiver

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples