Publications

Abstract

Learning can occur via trial and error; however, learning from conspecifics is faster and more efficient. Social animals can easily learn from conspecifics, but how do less social species learn? In particular, birds provide astonishing examples of social learning of vocalizations, while vocal learning from conspecifics is much less understood in mammals. We present a hypothesis aimed at solving an apparent paradox: how can harbor seals (Phoca vitulina) learn their song when their whole lives are marked by loose conspecific social contact? Harbor seal pups are raised individually by their mostly silent mothers. Pups' first few weeks of life show developed vocal plasticity; these weeks are followed by relatively silent years until sexually mature individuals start singing. How can this rather solitary life lead to a learned song? Why do pups display vocal plasticity at a few weeks of age, when this is apparently not needed? Our hypothesis addresses these questions and tries to explain how vocal learning fits into the natural history of harbor seals, and potentially other less social mammals. We suggest that harbor seals learn during a sensitive period within puppyhood, where they are exposed to adult males singing. In particular, we hypothesize that, to make this learning possible, the following happens concurrently: (1) mothers give birth right before male singing starts, (2) pups enter a sensitive learning phase around weaning time, which (3) coincides with their foraging expeditions at sea which, (4) in turn, coincide with the peak singing activity of adult males. In other words, harbor seals show vocal learning as pups so they can acquire elements of their future song from adults, and solitary adults can sing because they have acquired these elements as pups. We review the available evidence and suggest that pups learn adult vocalizations because they are born exactly at the right time to eavesdrop on singing adults. We conclude by advancing empirical predictions and testable hypotheses for future work.

Abstract

Objective

Researchers in animal cognition, psychophysics, and experimental psychology need to randomise the presentation order of trials in experimental sessions. In many paradigms, for each trial, one of two responses can be correct, and the trials need to be ordered such that the participant’s responses are a fair assessment of their performance. Specifically, in some cases, especially for low numbers of trials, randomised trial orders need to be excluded if they contain simple patterns which a participant could accidentally match and so succeed at the task without learning.
Results

We present and distribute a simple Python software package and tool to produce pseudorandom sequences following the Gellermann series. This series has been proposed to pre-empt simple heuristics and avoid inflated performance rates via false positive responses. Our tool allows users to choose the sequence length and outputs a .csv file with newly and randomly generated sequences. This allows behavioural researchers to produce, in a few seconds, a pseudorandom sequence for their specific experiment. PyGellermann is available at https://github.com/YannickJadoul/PyGellermann.

Abstract

Most bioacoustics software is used to analyse the already collected acoustics data in batch, i.e., after the data-collecting phase of a scientific study. However, experiments based on animal training require immediate and precise reactions from the experimenter, and thus do not easily dovetail with a typical bioacoustics workflow. Bridging this methodological gap, we have developed a custom application to live-monitor the vocal development of harbour seals in a behavioural experiment. In each trial, the application records and automatically detects an animal's call, and immediately measures duration and acoustic measures such as intensity, fundamental frequency, or formant frequencies. It then displays a spectrogram of the recording and the acoustic measurements, allowing the experimenter to instantly evaluate whether or not to reinforce the animal's vocalisation. From a technical perspective, the rapid and easy development of this custom software was made possible by combining multiple open-source software projects. Here, we integrated the acoustic analyses from Parselmouth, a Python library for Praat, together with PyAudio and Matplotlib's recording and plotting functionality, into a custom graphical user interface created with PyQt. This flexible recombination of different open-source Python libraries allows the whole program to be written in a mere couple of hundred lines of code

Abstract

Understanding cetacean distribution is essential for conservation planning and decision-making, particularly in regions subject to rapid environmental changes. Nevertheless, information on their spatiotemporal distribution is commonly limited, especially from remote areas. Species distribution models (SDMs) are powerful tools, relating species occurrences to environmental variables to predict the species' potential distribution. This study aims at using presence-only SDMs (MaxEnt) to identify suitable habitats for fin whales (Balaenoptera physalus) on their Nordic and Barents Seas feeding grounds. We used spatial-block cross-validation to tune MaxEnt parameters and evaluate model performance using spatially independent testing data. We considered spatial sampling bias correction using four methods. Important environmental variables were distance to shore and sea ice edge, variability of sea surface temperature and sea surface salinity, and depth. Suitable fin whale habitats were predicted along the west coast of Svalbard, between Svalbard and the eastern Norwegian Sea, coastal areas off Iceland and southern East Greenland, and along the Knipovich Ridge to Jan Mayen. Results support that presence-only SDMs are effective tools to predict cetacean habitat suitability, particularly in remote areas like the Arctic Ocean. SDMs constitute a cost-effective method for targeting future surveys and identifying top priority sites for conservation measures.