Review of recent carrying capacity models for bivalve culture and recommendations for research and management

Models and tools for assessing the carrying capacity of an area of interest for bivalve culture can be classified according to their level of complexity and scope. In this report, we discuss and outline four hierarchical categories of carrying capacity studies: physical, production, ecological, and social carrying capacity. The assessment of carrying capacity for progressively higher categories of models is based on a sound understanding of preceding categories. We discuss each in brief and the third in more detail as this is the level at which knowledge is the most lacking and for which science may make the most advances. (1) Physical carrying capacity may be assessed by a combination of hydrodynamic models and physical information, ideally presented and analysed within a Geographic Information System (GIS). (2) Most scientific effort to date has been directed towards modelling production carrying capacity and some of the resulting models have been used successfully to this end. Further development of these models should pay attention to (i) better modelling of feedback mechanisms between bivalve culture and the environment, (ii) a consideration of all steps in the culture process (seed collection, ongrowing, harvesting, and processing), and (iii) culture technique. (3) The modelling of ecological carrying capacity is still in its infancy. The shortcomings mentioned for models for production carrying capacity estimates are even greater for ecological carrying capacity models. GIS may be employed to consider interactions between culture activities and sensitive habitats. (4) It is recommended that social carrying capacity be evaluated only after the preceding levels have been completed so that an unbiased assessment is obtained. This however does not exclude direction from managers for scientists as to which factors (such as water clarity, specific habitats, etc.) should be evaluated. The use of expert systems to aid in management decisions is briefly discussed with a suggested application of a fuzzy expert system to this end.