https://commons.ru.ac.za/vital/access/manager/Index ${session.getAttribute("locale")} 5 https://commons.ru.ac.za/vital/access/manager/Repository/vital:1050 Wed 12 May 2021 20:30:21 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:34083 Wed 12 May 2021 18:55:08 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:994 Wed 12 May 2021 17:15:28 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:5229 0.05) amount of variability (63%) was explained by a predictive model incorporating these variables as investigations were constrained by small sample sizes and aggregated catch information. Scientific survey data provided multi-species information and highlighted the high proportion of non-native fish species in Eastern Cape impoundments. Gillnet catches were influenced primarily by species composition and were less subject to fluctuations induced by environmental factors. Overall standardised gillnet CPUE was influenced by surface area, conductivity and age of impoundment. Although the model fit was not significant at the p<0.05 level, 23% of the variability in the data was explained by a predictive model incorporating these variables. The presence of species which could be effectively targeted by gillnets was hypothesised to represent the most important factor influencing catch rates. Investigation of factors influencing CPUE in impoundments dominated by Clarias gariepinus and native cyprinids indicated that warmer, younger impoundments and smaller, colder impoundments produced higher catches of C. gariepinus and native cyprinids respectively. A predictive model for C. gariepinus abundance explained a significant amount of variability (77%) in CPUE although the small sample size of impoundments suggests that predictions from this model may not be robust. CPUE of native cyprinids was influenced primarily by the presence of Labeo umbratus and constrained by small sample size of impoundments and the model did not adequately explain the variability in the data (r² = 0.31, p>0.05). These results indicate that angling catch- and scientific survey data can be useful in providing predictions of fish abundance that are biologically realistic. However, more data over a greater spatial scale would allow for more robust predictions of catch rates. This could be achieved through increased monitoring of existing resource users, the creation of a centralised database for catch records from angling competitions, and increased scientific surveys of South African impoundments conducted by a dedicated governmental function.]]> Wed 12 May 2021 16:19:14 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:997 Wed 12 May 2021 16:04:59 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:1117 Thu 13 May 2021 07:33:16 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:1097 Thu 13 May 2021 05:40:21 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:38121 Thu 13 May 2021 05:26:02 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:955 Thu 13 May 2021 04:08:04 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:3331 Thu 13 May 2021 03:45:11 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:1040 Thu 13 May 2021 03:02:00 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:995 Thu 13 May 2021 02:08:15 SAST ]]> https://commons.ru.ac.za/vital/access/manager/Repository/vital:30561 Thu 13 May 2021 00:14:42 SAST ]]>