| Autor: |
Michael A. Pardo, David S. Lolchuragi, Joyce Poole, Petter Granli, Cynthia Moss, Iain Douglas-Hamilton, George Wittemyer |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Royal Society Open Science, Vol 11, Iss 9 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2054-5703 |
| DOI: |
10.1098/rsos.241264 |
| Popis: |
Vocalizations often vary in structure within a species, from the individual to population level. Vocal differences among social groups and populations can provide insight into biological processes such as vocal learning and evolutionary divergence, with important conservation implications. As vocal learners of conservation concern, intraspecific vocal variation is of particular interest in elephants. We recorded calls from individuals in multiple, wild elephant social groups in two distinct Kenyan populations. We used machine learning to investigate vocal differentiation among individual callers, core groups, bond groups (collections of core groups) and populations. We found clear evidence for vocal distinctiveness at the individual and population level, and evidence for much subtler vocal differences among social groups. Social group membership was a better predictor of call similarity than genetic relatedness, suggesting that subtle vocal differences among social groups may be learned. Vocal divergence among populations and social groups has conservation implications for the effects of social disruption and translocation of elephants. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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