Animals that co-occur in sympatry with multiple closely-related species use reproductive mate attraction signals not only to assess the quality of a potential conspecific mate (sexual selection), but also to discriminate conspecifics from heterospecifics (species recognition). However, the extent to which sexual selection and species recognition may interact, or even conflict, is poorly known. Neotropical electric fish offer unrivaled opportunities for understanding this problem. They generate simple, stereotyped mate attraction signals that are easy to record and quantify, and that are well-understood from the neurobiological perspective. Additionally, they live in electrically-crowded environments, where multiple congeners live and reproduce in close proximity. This dissertation reports an investigation of electric signal diversity and reproductive life history in a nine-species assemblage of the electric fish genus Brachyhypopomus from the upper Amazon. A year-long quantitative sampling program yielded a library of electric signal recordings from >3,000 individuals and an accompanying collection of preserved specimens from which suites of informative life history traits were measured. These data were used to understand basic reproductive biology, and to describe sexually dimorphic and interspecific diversity in electric signals. By integrating approaches from ecology, physiology, and evolutionary biology, novel perspectives are provided on: 1. how sexual selection and species recognition interact to shape signal diversity and the occupation of signal space in multi-species animal communities; 2. how extreme seasonal variation in Amazonian ecosystems influences trade-offs in the allocation of reproductive resources – including mate attraction signals, and; 3. how environmental variation shapes general life-history traits in a diverse tropical animal assemblage.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-6758 |
| Date | 01 January 2017 |
| Creators | Waddell, Joseph |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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