An Honest PUC

Back in January, my brother Nate got several BirdWeather PUCs, small outdoor devices that listen for bird songs, and identify the species responsible, similar to the popular Merlin phone app. He gave one to our parents, one to Marika and me, and kept one for himself. If you follow those links, you can see each PUC uploads its observations to a central server, which can be queried through an API. Nate used this to set up a daily email giving the previous day's species counts for each of our stations, and I was curious if I could apply some of the data analysis techniques I've learned to the results.

The first thing I wanted to try was identifying trends in the time of day each bird is heard. To do this, we can use an idea called data folding. This is similar to the idea of a Fourier transform, where we're dealing with periodic data, but we're only interested in a single period: 1 day. If we split our months of data into single-day segments and stack them on top of each other, we can get better statistics about when each bird is heard. There are too many individual species to look at all of them, so I considered finding a way to group them. One idea was to use their taxonomy, but depending on the level I chose, I'd get either one big group, or a group for every bird. Going back to the better statistics idea, I decided to just plot the ones with more than 200 total detections:

This is called a violin plot, often used to show statistical distributions like these. The end caps show the max/min values, and the bulges show the more frequent times. You can see that most peak around dawn hours, but a few are heard throughout the day.

In the daily emails Nate set up, I noticed that for a long time I only saw the Carolina wren show up in Florida, but then it started popping up in Massachusetts as well. I wondered if I was seeing a spring migration, so I got the data from all 3 of our stations, and looked at the number of wrens for each day:

This seems to show it was just my imagination: The wrens are much more frequent in Florida overall, and there doesn't appear to be a trend toward MA over time.

In the first plot I showed, you may have noticed the "Engine" entry. It turns out the PUC has several non-bird sounds it recognizes, and it picked up the highway traffic next door to us. I was curious to see what other non-bird detections it had made. The reported data gets a "confidence" rank based on both how close the sound was to the model, and how likely it is to hear that sound in the location. I split up the detections below on those rankings:

It's reassuring to see the few "gun" detections don't rise above uncertain, though the siren counts are quite high (and I'm assuming that refers to the emergency-vehicle type, not luring-sailors-to-doom type). Seeing these makes me curious what other sounds it can recognize. Thanks for this cool gift, Nate!

[Edit: I noticed this post is getting lots of traffic from the BirdWeather community. Maybe someone *cough*Nate*cough* shared this post there, but anyway, here's my code!]