All cases of exact, close, or not-so-close “aww" reciprocations are shown in Table 1. Of these, 19 cases were audio-only, and 4 were video with audio. The concentration of more recent recordings arises from more citizen scientists doing more recordings and with better equipment, and because the Macaulay Library is becoming better known.
To quantify correlation between "aww" sent and replied, a non-parametric test is needed since there are non-normal distributions of both sent and replied "aww" numbers. The proper test appears to be the Spearman's Rank Correlation [22], and so we plug the pairs of numbers into the RStudio statistical package [23] and call on the Spearman:
# Let x be the number of aww sent, y replied
x = c(1, 1, 1, 2, 1, 3, 3, 2, 2, 2, 4, 4, 3, 1, 1, 2, 2, 1, 2, 1, 3, 3, 1, 3, 1, 4, 4, 3, 6, 4, 4, 4, 3, 4, 2, 3, 2, 3, 1, 2, 2, 3, 4, 3, 3, 4)
y = c(1, 1, 1, 2, 1, 4, 3, 3, 3, 2, 4, 3, 3, 1, 1, 2, 2, 1, 2, 1, 2, 2, 1, 2, 1, 3, 2, 3, 4, 4, 3, 4, 3, 2, 2, 1, 3, 2, 1, 1, 1, 3, 3, 2, 3, 3)
result = cor.test(x, y, method = "spearman")
print(result)
To avoid pseudoreplication -- e.g. the 1990 Oregon ravens counting as 4 exact reciprocations -- let us reduce each such case to a single averaged pair of "aww" sent and replied. E.g. "1–1, 1–1, 2–2, 1–1" becomes "1.25–1.25"; "3 − 1, 2–3, 3 − 2" becomes "3-1.7" to reflect the average of 1.3 difference in sent and replied “aww", and so on. Though this reduces our sample from 46 to 23 sent-and-replied pairs, the correlation emerges even more strongly, with RS = 0.852, and p < < 0.001.