Project Team: James Mugodo, Richard Norris, Peter Liston, Simon Linke, Sue Nichols, Mark Kennard
The habitat assessment approach to be undertaken by the project is to build a predictive model(s) based on the habitat requirements of native fish. The models would predict local scale (fish) habitat from larger catchment scale variables. The models could predict habitat features that should occur at a site within the catchment and thus provide a valuable tool to manage the survival, breeding and passage requirements of fish populations. Table 1 lists the fish species chosen for the focus of the D2 habitat assessment project.
These species were chosen because they represent a range of fish types and cover a range of issues, which may collectively cover a comprehensive range of habitat requirements:
- Some of the fish are icon or endangered species;
- the subset display a range of feeding approaches (thus may have differing habitat requirements in this regard);
- they are both migrating and non-migrating (thus operating at a variety of geographic scales);
- The adult size of the various fish species ranges from small to large (very small fish may operate on a similar scale to invertebrates).
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Depending on the various habitat requirements identified, the predictive models may be specific for particular species or more general for fish communities. The models are also likely to be regionally specific. To build the predictive models we must first identify the habitat requirements of the selected fish species, and possibly determine which are the limiting or most important features for the various life stages. These habitat preferences can then be translated into geomorphic terms and other variables that can be predicted from larger scale catchment features. Following that we can then draw together data that are contain within the myriad of databases that exist across the country. The outcome of the meeting is contained within the following "to do" list, which has been compiled to achieve these aims.
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Professor Richard Norris 