Automatic counting
Bird counts using medic software have already been described by Pérez-García (2012), who used the UHTSCSA Image Tool 3.0 software to perform a census of starlings. This software does not work everywhere, however: e.g. it cannot be installed in the Windows 10 operating system. Other free software dedicated to birds has been described by Descamps et al. (2011), who used this to count flamingos in breeding colonies. This latter software has turned out to be quite difficult to use: one serious difficulty is the use of French as the basic language and the inability to change the language version. In contrast, DotCount v1.2 is easy to use, but its disadvantage is the closed source, so that it cannot be used to create plugins or updates to enhance its functionality; moreover, the latest version of this stems from as long ago as 2012. In the future, the use of neural networks and machine learning in wildlife studies will undoubtedly increase in importance (e.g. Villa et al. 2017, Tabak et al. 2019). Clearly, there are many different possibilities and solutions for automatic bird counting, but at present, ImageJ / Fiji (Grishagin 2015) seems the best choice. It is an open-source platform, so several programmers and biologists can work together to create new plugins. Some of them already use neural network-based algorithms (Buchholz et al. 2020).
The automatic counting methods using the ImageJ / Fiji platform proved to be the best in our study, as they were the fastest and maintained the precision of the results (Tables 3 and 4, Fig. 2). For small and medium bird concentrations, we recommend using the Analyze Particles tool in the ImageJ / Fiji software program. This does require pre-treatment of each image each time, but with some practice, one can do this quite efficiently and the results will be precise.
In the case of solutions using neural network algorithms, a one-off count takes a very short time (23 seconds on average, Table 4), but to achieve this state requires a lot of prior preparation, and the images require some preliminary assumptions. The computer on which the machine learning will be conducted must have the appropriate hardware. Then the images have to be scaled in such a way that the objects are roughly the same size, and the learning time of the neural network can take up to several dozen hours. This method is therefore recommended in situations where a lot of data (images) have been acquired from long-term monitoring programmes, in large breeding colonies and in non-breeding concentrations.
Acknowledgments
The author would like to thank Zbigniew Kajzer, Łukasz Ławicki, Aleksandra Marchowska and Paweł Stańczak, who helped him with his field work, and Joran Deschamps and Deborah Schmidt for their help in data analysis using neural networks.
Author’s contributions
D.M. is responsible for all parts of this work.
Data availability statement
The raw data on the basis of which the analyses were carried out are attached to the article as supplementary materials.
ORCID
Dominik Marchowskihttps://orcid.org/0000-0001-7508-9466
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