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Towards adaptive approaches to management of the South African abalone <i>haliotis midae</i> fishery
Abstract
The South African abalone Haliotis midae resource is widely perceived as being under threat of overexploitation as a result of increased poaching. In this paper, reservations are expressed about using catch per unit effort as the sole index of abundance when assessing this fishery, particularly because of the highly aggregatory behaviour of the species. A fishery-independent survey has been initiated and is designed to provide relative indices of abundance with CVs of about 25% in most of the zones for which Total Allowable Catchs (TACs) are set annually for this fishery. However, it will take several years before this relative index matures to a time-series
long enough to provide a usable basis for management. Through a series of simple simulation models, it is shown that calibration of the survey to provide values of biomass in absolute terms would greatly enhance the
value of the dataset. The models show that, if sufficient precision (CV 50% or less) could be achieved in such a calibration exercise, the potential for management benefit is improved substantially, even when using a relatively simple management procedure to set TACs. This improvement results from an enhanced ability to detect resource declines or increases at an early stage, as well as from decreasing the time period until the survey index becomes useful. Furthermore, the paper demonstrates that basic modelling techniques could usefully indicate which forms of adaptive management experiments would improve ability to manage the resource, mainly through estimation of the level of precision that would be required from those experiments. The results of this study are particularly
applicable to fishing zones for which there are insufficient other data to perform a standard stock assessment.
long enough to provide a usable basis for management. Through a series of simple simulation models, it is shown that calibration of the survey to provide values of biomass in absolute terms would greatly enhance the
value of the dataset. The models show that, if sufficient precision (CV 50% or less) could be achieved in such a calibration exercise, the potential for management benefit is improved substantially, even when using a relatively simple management procedure to set TACs. This improvement results from an enhanced ability to detect resource declines or increases at an early stage, as well as from decreasing the time period until the survey index becomes useful. Furthermore, the paper demonstrates that basic modelling techniques could usefully indicate which forms of adaptive management experiments would improve ability to manage the resource, mainly through estimation of the level of precision that would be required from those experiments. The results of this study are particularly
applicable to fishing zones for which there are insufficient other data to perform a standard stock assessment.
