https://vital.seals.ac.za/vital/access/manager/Index ${session.getAttribute("locale")} 5 https://vital.seals.ac.za/vital/access/manager/Repository/vital:8632 Wed 12 May 2021 23:20:38 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38553 Wed 12 May 2021 18:48:16 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:4756 Wed 12 May 2021 17:10:03 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38406 Wed 12 May 2021 16:43:51 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:21077 Wed 12 May 2021 16:34:08 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38602 Wed 12 May 2021 16:14:27 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41487 Wed 12 May 2021 14:43:34 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41458 0.5, suggesting that further development of the assay format to identify GEF and GAP inhibitors may be feasible. In the case of the colorimetric assay, robust signals could be detected and the assay was useful for detecting the activation status of ARF1. However, although the activation of ARF1 by the Sec7 domains of the BIG1 and ARNO was detectable, signals were not robust enough to employ in screening campaigns.]]> Wed 12 May 2021 14:25:48 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42049 Wed 12 May 2021 14:18:20 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:30711 Thu 13 May 2021 15:04:39 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38656 Thu 13 May 2021 10:12:28 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38542 Thu 13 May 2021 08:43:43 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38481 Thu 13 May 2021 07:39:38 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:4700 Thu 13 May 2021 07:28:05 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38635 Thu 13 May 2021 05:23:36 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:20754 Thu 13 May 2021 04:50:09 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38521 Thu 13 May 2021 02:18:58 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:5160 Thu 13 May 2021 01:32:50 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:27798 500 km) and modelled with an ordinal logistic regression. Species, life-stage (juvenile/adult) and time- at-liberty were included as predictor variables. Model coefficients were then included in a cluster analysis, which produced two primary groupings of species (Category I and II), with two sub-groupings (Category IIa and IIb). Category I species were characterised by wide-ranging movements, greatest median recapture length and highest trophic levels. Category II species were characterised by residency, lower median recapture length and lower trophic levels. These findings have implications for fisheries management. Exploitation of resident species may lead to localised depletion, but their diffuse spatial distribution may offer some resilience. In contrast, even localised targeting of migratory species may pose a population level risk if individuals are known to aggregate. Life-cycle diversity or intra-population variability describes the existence of alternative strategies or tactics among coexisting individuals within an animal population (Chapter 5). Partial migration is a specific case of life-cycle diversity where coexisting groups exhibit either resident or migratory (wide-ranging) behaviour within a single population. Mark- recapture data collected under the auspices of the ORICFTP were used to investigate the occurrence and nature of life-cycle diversity in the movement behaviour of five non- diadromous fish species around the coastline of southern Africa. Among the five species were three teleosts (Category I and IIa) and two elasmobranchs (Category I). A fish was considered to have remained resident if recaptured within 0-5 km after 365 days at liberty. A fish was considered to have undertaken a wide-ranging movement if recaptured more than 50 km away from the release site in 365 days or less. A total of 1848 individuals from the five study species were recaptured during the study, of which 73% of the observations were classified as being resident. Binomial logistic regression confirmed that species, life-stage (juvenile/adult) and ecoregion were significant (p < 0.001) predictors of the probability of wide-ranging behaviour. A Gaussian model confirmed that species and ecoregion were also significant (p < 0.001) predictors of direction and distance of wide-ranging movement. However, the direction and distance of wide-ranging movements in juveniles did not differ significantly (p > 0.05) to that of adults. The median growth rate was mostly greater in wide- ranging individuals; however, this was only statistically significant (p < 0.05) in two cases. These findings provide unequivocal evidence of life-cycle diversity in five fish species, with vastly different life-histories. This ecological phenomenon may provide species resilience at the population level and needs to be considered in fisheries management initiatives. The movement of fishes is a fundamental aspect to consider when designing fishery management regimes. Unfortunately, traditional management strategies have often disregarded movement behaviour to the detriment of fish populations (Chapter 6). As a case study, the management of Lichia amia (Category I: wide-ranging) was evaluated in the context of its movement behaviour. Long-term catch-per-unit-effort (CPUE) datasets were examined for three South African recreational fishery sectors. The CPUE was standardised using generalized linear models (delta-Gamma/hurdle approach) to reduce the effect of factors other than abundance. Factors that were available for this purpose were year, month and locality/zone. Year was included in every model as the primary objective was to detect trends in abundance over time. Although standardised CPUE for all sectors showed an overall long-term decline, there was considerable variability in trends between the different recreational sectors and between datasets. Contrasting trends between competitive shore angling and general shore angling datasets were ascribed to hyperstability in competitive CPUE data. Hyperstability in this case was mostly influenced by rapidly improving technology, techniques and communication networks amongst competitive anglers. Month and locality were significant factors explaining the probability of catching L. amia. This suggests that the predictable aggregatory behaviour of this species could further compound the observed CPUE hyperstability. Although the CPUE responded positively for six years after implementation of the first minimum size and bag limits, and for one year after the decommercialisation of the species, these regulations and their amendments failed to arrest a long-term decline in the CPUE despite the ample evidence for hyperstability. It is clear from this case study that the predictable nature of wide-ranging behaviour in L. amia has made the population vulnerable to exploitation. This has led to the demise in the population, which could have been worse if not for the occurrence of intra-population variability in its movement behaviour, which may provide some measure of resilience.]]> Thu 13 May 2021 00:49:53 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:28413 Thu 04 May 2023 11:21:57 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:27408 Fri 19 May 2023 10:05:04 SAST ]]>