Audio Birding

This spring, I redeployed my AudioMoth acoustic recorder to explore which bird species were using, or simply passing through, my backyard during peak spring migration. As in previous years, I programmed the device to record for three hours each morning, from 5:30 to 8:30 am, beginning on May 12 and continuing through June 15. If you’d like more background information, check out my earlier blog post. Below are some of my initial findings for this year.

d3 = require("d3@7")
format = d3.format(".01%")

// load data for 2025
bird_freq_25 = await FileAttachment("data/may_june_chorus_2025.csv").csv({typed: true});
bird_freq_25_filt = bird_freq_25.filter((d) => d.freq > 0.001).map(d => { d.year = Number(2025); return d; })

// load data for comparison
bird_pct = await FileAttachment("data/comparison_2025.csv").csv({typed: true});

// OcclusionY adds an initializer that shifts nodes vertically with a tiny force simulation.
occlusionY = ({radius = 6.5, ...options} = {}) => Plot.initializer(options, (data, facets, { y: {value: Y}, text: {value: T} }, {y: sy}, dimensions, context) => {
  for (const index of facets) {
    const unique = new Set();
    const nodes = Array.from(index, (i) => ({
      fx: 0,
      y: sy(Y[i]),
      visible: unique.has(T[i]) // remove duplicate labels
        ? false
        : !!unique.add(T[i]),
      i
    }));
    d3.forceSimulation(nodes.filter((d) => d.visible))
      .force("y", d3.forceY(({y}) => y)) // gravitate towards the original y
      .force("collide", d3.forceCollide().radius(radius)) // collide
      .stop()
      .tick(20);
    for (const { y, node, i, visible } of nodes) Y[i] = !visible ? NaN : y;
  }
  return {data, facets, channels: {y: {value: Y}}};
})

// d3 plot
Plot.plot({
  marginLeft: 145,
  fill: "#021d44",
  x: {axis: "top", percent: true, label: "Frequency"},
  y: {label: null},
  marks: [
    Plot.barX(bird_freq_25_filt, {x: "freq", y: "common_name", fill: "#00467F", sort: {y: "x", reverse: true}}), 
    Plot.text(bird_freq_25_filt, {x: "freq", y: "common_name", text: d => format(d.freq), textAnchor: "start", dx: 3, filter: d => d.freq <= 0.02, fill: "currentColor"}), 
    Plot.text(bird_freq_25_filt, {x: "freq", y: "common_name", text: d => format(d.freq), textAnchor: "end", dx: -3, filter: d => d.freq > 0.02, fill: "white" }),
    Plot.ruleX([0])
  ]
})

Figure 1: Relative frequency of detected bird calls from AudioMoth recordings.

To better understand how the soundscape changed between 2024 and 2025, I focused on species with a noticeable presence (those that made up at least 2% of the relative frequency in either year). This threshold helped to spotlight species that were consistently active or showed meaningful changes, while filtering out those that were incidental or rare.

So, what stood out?

// d3 plot
Plot.plot({
  height: 540,
  marginLeft: 90,
  marginRight: 88,
  marginBottom: 5,
  x: {axis: "top", type: "ordinal", tickFormat: "", inset: 90, label: null},
  y: {axis: null, inset: 20},
  marks: [
    Plot.line(bird_pct, {x: "year", y: "pct", z: "common_name", stroke: "common_name"}),
    d3.groups(bird_pct, (d) => d.year === 2024)
      .map(([left, bird_pct]) =>
        Plot.text(bird_pct, occlusionY({
          x: "year",
          y: "pct",
          text: left
            ? (d) => `${d.common_name} ${d.pct}%`
            : (d) => `${d.pct}% ${d.common_name}`,
          textAnchor: left ? "end" : "start",
          dx: left ? -5 : 5,
          fill: "common_name",
          fontSize: 14,
          radius: 8
        }))
      )
  ]
})

Figure 2: Changes in the relative frequency of detected bird calls over time.

The American Robin was the only bird species that was more vocal or more frequently detected in 2025, suggesting increased activity or possibly a growing local population. Others declined, which might reflect behavioral changes, shifts in local habitat use, or even alterations in migratory timing and stopover site preferences (possibly steering birds away from my neighborhood altogether).

By looking at these year-to-year differences, I’m starting to understand how the bird community in my own backyard is changing. One surprise in 2025 was the first-ever detection of a House Wren, which I continue to hear every morning :) And I’m always amazed at all of the wood warbler species that stopover during migration (often very breifly). This year, I picked up Blackpolls, Black-throated Greens, and Ovenbirds. The detection of some Red-eyed Vireos might also suggest migration was earlier this year compared to last.

All of this is a fun reminder that even a small, suburban backyard can be part of a much bigger picture. The AudioMoth lets me witness these moments I would have otherwise missed.