Abstract
Open science approaches enable and facilitate the investigation of many scientific questions in bioacoustics, such as studies on the temporal and spatial evolution of song, as in vocal dialects. In contrast to previous dialect studies, which mostly focused on songbird species with a small repertoire, here we studied the common nightingale (Luscinia megarhynchos), a bird species with a complex and large repertoire. To study dialects on the population level in this species, we used recordings from four datasets: an open museum archive, a citizen science platform, a citizen science project, and shared recordings from academic researchers. We conducted the to date largest temporal and geographic dialect study of birdsong including recordings from 1930 to 2019 and from 13 European countries, with a geographical coverage of 2,652 km of linear distance. To examine temporal stability and spatial dialects, a catalog of 1,868 song types of common nightingales was created. Instead of dialects, we found a high degree of stability over time and space in both, the sub-categories of song and in the occurrence of song types. For example, the second most common song type in our datasets occurred over nine decades and across Europe. In our case study, open and citizen science data proved to be equivalent, and in some cases even better, than data shared by an academic research group. Based on our results, we conclude that the combination of diverse and open datasets was particularly useful to study the evolution of song in a bird species with a large repertoire.
Highlights
Open science practices such as open data (OD), citizen science (CS), and data sharing may open new avenues for answering novel kinds of research questions
The occurrences of song types differed among the four datasets. 78% of songs types appeared in only one of the datasets, 9% of songs types were found in two of the datasets, 5% of songs types occurred in three datasets and 8% of songs types were in all four datasets
By directly comparing the datasets, we have shown that OD and CS recordings are well suited – and in some cases better suited – for studying song variation in bird species with a large repertoire than data collected by an academic research group, because they were collected over a shorter period of time and in a much smaller geographic range
Summary
Open science practices such as open data (OD), citizen science (CS), and data sharing may open new avenues for answering novel kinds of research questions. Usage of CS data is not yet well established in ornithology (Weisshaupt et al, 2021) due to a lack of information and quality concerns about data availability, data bias, and data generation (Hochachka et al, 2012; Lukyanenko et al, 2016). This quality concern is why CS data are often not made publicly available in the sense of open science. To aid in establishing such a representative case study, we investigated temporal and geographical song variations, so-called dialects, in a bird species
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