- Title
- Generalized linear models, with applications in fisheries research
- Creator
- Sidumo, Bonelwa
- Subject
- Western mosquitofish
- Subject
- Analysis of variance
- Subject
- Fisheries Catch effort South Africa Sundays River (Eastern Cape)
- Subject
- Linear models (Statistics)
- Subject
- Multilevel models (Statistics)
- Subject
- Experimental design
- Date Issued
- 2018
- Date
- 2018
- Type
- text
- Type
- Thesis
- Type
- Masters
- Type
- MSc
- Identifier
- http://hdl.handle.net/10962/61102
- Identifier
- vital:27975
- Description
- Gambusia affinis (G. affinis) is an invasive fish species found in the Sundays River Valley of the Eastern Cape, South Africa, The relative abundance and population dynamics of G. affinis were quantified in five interconnected impoundments within the Sundays River Valley, This study utilised a G. affinis data set to demonstrate various, classical ANOVA models. Generalized linear models were used to standardize catch per unit effort (CPUE) estimates and to determine environmental variables which influenced the CPUE, Based on the generalized linear model results dam age, mean temperature, Oreochromis mossambicus abundance and Glossogobius callidus abundance had a significant effect on the G. affinis CPUE. The Albany Angling Association collected data during fishing tag and release events. These data were utilized to demonstrate repeated measures designs. Mixed-effects models provided a powerful and flexible tool for analyzing clustered data such as repeated measures data and nested data, lienee it has become tremendously popular as a framework for the analysis of bio-behavioral experiments. The results show that the mixed-effects methods proposed in this study are more efficient than those based on generalized linear models. These data were better modeled with mixed-effects models due to their flexibility in handling missing data.
- Format
- 179 pages
- Format
- Publisher
- Rhodes University
- Publisher
- Faculty of Science, Statistics
- Language
- English
- Rights
- Sidumo, Bonelwa
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