You're right to mention this. Lossless audio is preferred for analyst software. Even good MP3s tend to top out around 16kHz.
The quality of the recording will also be dependent on the microphones used and their frequency range.
When we have analysed animal sounds it's useful to play the sample slower, pitched down. Having those higher frequencies recorded well, above 16kHz, make a huge difference to the signals information.
It's kinda hard to compare the different spectral representations when they're zoomed and cropped differently.
Spectrograms can be misleading, in a few different ways. Magnitude FFTs discard phase, which we can hear. And our eyes tend to fixate on the peaks, but the noise floor between harmonics in speech had a big impact on perceived quality. Choice of color scheme and gradient changes how we look at the spectrogram: they can emphasize mathematical or coding artifacts we wouldn't hear, or hide things which we can hear. At the end of the day, we don't hear with our eyes... So a spectrogram is a tool for looking at audio, but not always an 'honest' one. So I'm a bit suspect of pouring over slightly different spectrograms, and worrying about which ones look better aesthetically.
Also the relative phase of multiple tones affects what the actual shape looks like. A classic example is a square wave. Yes, it needs all odd harmonics at a sinc(f) magnitude, but it also needs all of those harmonics at specific phases.
You record a portion of the song, and it uses machine learning to analyse it and tell you the bird with a confidence figure. Works really well.
Once you've identified the bird, you can then listen to a variety of additional recordings on https://www.xeno-canto.org/ which I believe is one of the sources used to train the machine learning model.
