Using the Data-Compression Method for Studying Hunting Behavior in Small Mammals.

biological texts complexity data compression hunting stereotype insectivorous rodents

Journal

Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874

Informations de publication

Date de publication:
04 Apr 2019
Historique:
received: 16 12 2018
revised: 27 03 2019
accepted: 01 04 2019
entrez: 3 12 2020
pubmed: 4 4 2019
medline: 4 4 2019
Statut: epublish

Résumé

Using the data-compression method we revealed a similarity between hunting behaviors of the common shrew, which is insectivorous, and several rodent species with different types of diet. Seven rodent species studied displayed succinct, highly predictable hunting stereotypes, in which it was easy for the data compressor to find regularities. The generalist Norway rat, with its changeable manipulation of prey and less predictable transitions between stereotype elements, significantly differs from other species. The levels of complexities of hunting stereotypes in young and adult rats are similar, and both groups had no prior experience with the prey, so one can assume that it is not learning, but rather the specificity of the organization of the stereotype that is responsible for the nature of the hunting behavior in rats. We speculate that rodents possess different types of hunting behaviors, one of which is based on a succinct insectivorous standard, and another type, perhaps characteristic of generalists, which is less ordered and is characterized by poorly predictable transitions between elements. We suggest that the data-compression method may well be more broadly applicable to behavioral analysis.

Identifiants

pubmed: 33267082
pii: e21040368
doi: 10.3390/e21040368
pmc: PMC7514852
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Russian Foundation for Basic Research
ID : 17-04-00702, 18-29-03005
Organisme : Russian Academy of Sciences
ID : 2013-2021, АААА-А16-АААА-А16-116121410120-0; АААА-А18-118042690110-1 (0109-2019-0003)

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Auteurs

Zhanna Reznikova (Z)

Institute of Animal Systematics and Ecology, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630091, Russia.
Novosibirsk State University, Novosibirsk 630090, Russia.

Jan Levenets (J)

Institute of Animal Systematics and Ecology, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630091, Russia.

Sofia Panteleeva (S)

Institute of Animal Systematics and Ecology, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630091, Russia.
Novosibirsk State University, Novosibirsk 630090, Russia.

Anna Novikovskaya (A)

Institute of Animal Systematics and Ecology, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630091, Russia.

Boris Ryabko (B)

Novosibirsk State University, Novosibirsk 630090, Russia.
Institute of Computational Technologies, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia.

Natalia Feoktistova (N)

Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia.

Anna Gureeva (A)

Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia.

Alexey Surov (A)

Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow 119071, Russia.

Classifications MeSH