Pitfalls and benefits of involving industry in fisheries research: A case study of the live reef fish industry in Queensland, Australia

Including collaboration with industry members as an integral part of research activities is a relatively new approach to fisheries research. Earlier approaches to involving fishers in research usually involved compulsory accommodations of research, such as through compulsory observer programs, in which fishers were seen as subjects of rather than participants in research. This new approach brings with it significant potential benefits but also some unique issues both for the researchers and the participating industry members. In this paper we describe a research project involving the Queensland Coral Reef Finfish Fishery that originated from industry and community concerns about changes in marketing practices in an established commercial line fishery. A key aspect of this project was industry collaboration in all stages of the research, from formulation of objectives to assistance with interpretation of results. We discuss this research as a case study of some of the issues raised by collaboration between industry and research groups in fisheries research and the potential pitfalls and benefits of such collaborations for all parties. A dedicated liaison and extension strategy was a key element in the project to develop and maintain the relationships between fishers and researchers that were fundamental to the success of the collaboration. A major research benefit of the approach was the provision of information not available from other sources: 300 days of direct and unimpeded observation of commercial fishing by researchers; detailed catch and effort records from a further 126 fishing trips; and 53 interviews completed with fishers. Fishers also provided extensive operational information about the fishery as well as ongoing support for subsequent research projects. The time and resources required to complete the research in this consultative framework were greater than for more traditional, researcher-centric fisheries research, but the benefits gained far outweighed the costs.

Assessment of functional forms of crop yield loss models of invasive plant species applied in decision support tools and bioeconomic modelling

AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.