Crowd intelligence can discern between repertoires of killer whale ecotypes

Abstract

Call classifications by human observers are often subjective yet they are critical to studies of animal communication, because only the categories that are relevant for the animals themselves actually make sense in terms of correlation to the context. In this paper we test whether independent observers can correctly detect differences and similarities in killer whale repertoires. We used repertoires with different a priori levels of similarity: from different ecotypes, from different oceans, from different populations within the same ocean, and from different local subpopulations of the same population. Calls from nine killer whale populations/subpopulations were pooled into a joint sample set, and eight independent observers were asked to classify the calls into separate categories. None of the observers' classifications strongly followed the known phylogeny of the analyzed repertoires. However, some phylogenetic relationships were reflected in the classifications substantially better than others. Most observers correctly separated the calls from two North Pacific ecotypes. Call classifications averaged across multiple observers reflected the known repertoire phylogenies better than individual classifications, and revealed the similarity of repertoires at the level of subpopulations within the same population, or closely related populations.

A comparison of Northeast Atlantic killer whale (Orcinus orca) stereotyped call repertoires

“…Danishevskaya et al (2020) found that human observers distinguished Icelandic and Norwegian killer whale calls but clustered them with those of North Pacific residents. To date only one study indicates a link between Icelandic and Norwegian killer whale call repertoires: Strager (1995) found two matches between call types recorded off Norway and those recorded off East Iceland by Moore et al (1988).…”

“…However, the most recent photographic data sets collected in both Iceland and Norway have not been compared yet and this ongoing work might shed light on the present-day connectivity between these populations. Danishevskaya et al (2020) found that human observers distinguished Icelandic and Norwegian killer whale calls but clustered them with those of North Pacific residents. To date only one study indicates a link between Icelandic and Norwegian killer whale call repertoires: Strager (1995) found two matches between call types recorded off Norway and those recorded off East Iceland by Moore et al (1988).…”

“…More recently, Shamir et al (2014) investigated the performance of an automated image comparison method to classify calls recorded from killer whales in Iceland and Norway and found that the algorithm automatically separated the calls between the two locations without prior information on their origin. Danishevskaya et al (2020) investigated whether independent observers could correctly detect differences in repertoires of killer whale populations from different ecotypes, different oceans, and from different subpopulations of the same population. While both North Pacific resident killer whales and North Atlantic killer whales were easily distinguished from North Pacific transient killer whales, Icelandic and Norwegian call repertoires were difficult to distinguish from North Pacific resident type killer whales.…”

Passive acoustic monitoring of killer whales (Orcinus orca) reveals year-round distribution and residency patterns in the Gulf of Alaska

“…Different killer whale populations produce unique repeated pulsed calls, called “discrete” or “stereotyped” calls 27 , 33 , 34 , 37 , 38 . Discrete calls can be reliably distinguished aurally and visually on a spectrogram and used to differentiate among populations 37 , 39 – 42 . Transient killer whale calls are also often characterized by an audible quavering of the fundamental sound frequencies and fewer call syllables 40 , 43 .…”

A novel simulation framework for crowd co-decisions

“…The phenomenon of crowd intelligence can be observed in our daily lives as the proverb goes "two heads are better than one" and "everybody's business is nobody's business" (Danishevskaya et al, 2018). Especially, with the rapid development of the network technology, crowd intelligence becomes more complicated and universal, as human, enterprises, governments, equipment and articles become increasingly intelligent, and these intelligent agents to connect each other to form numerous crowd network systems, such as e-commerce platforms, networked supply chains, Wikipedia and network elections (Bonney et al, 2009).…”