Benthic habitat dynamics and models on Australias North West Shelf

Brief description

As management of marine living resource moves beyond simple single species resource utilisation concerns to ecosystem-based management, consideration of habitat dynamics is becoming an integral part of marine resource management. Previous studies have found that habitat can play a critical role in both single species and community level dynamics of species of commercial concern (Sainsbury, 1987; Sainsbury, 1988; Auster & Malatesta, 1995; Freese et al. 1999; Lindholm et al. 1999; Jackson et al. 2000; Sainsbury et al. 2000). Moreover, benthic habitat is becoming a conservation concern in its own right (Environment Protection and Biodiversity Conservation Act 1999). Useful first steps in understanding local benthic habitat dynamics is to collect observation (preferably through time) of the benthos and then to attempt to create dynamic models that capture the broadscale dynamics of the habitat of interest. Just such an exercise was undertaken for the major benthic habitat types in the North West Shelf of Australia (specifically epibenthic, mainly sponge, habitats, seagrass, macroalgae and mangroves). Between 1983 and 1997 photographic data on benthic habitats were collected on the North West Shelf of Australia by CSIRO Marine Research. These data were used to calculate proportional coverage of small (<25 cm) and large (>25 cm) epibenthos on the seabed between depths of 20 and 200 m. These observations and the fisheries effort data for the Taiwanese (1973 to 1981) and domestic fleets (1987 to 1997) were pooled onto a spatial grid of 10 by 10 nautical minutes with a temporal scale of a year. A multivariate analysis of the main factors associated with the distribution of the benthic habitats was undertaken (as a guide for factors to include in the final habitat dynamics model). The observations suggested that there was a strong depth-dependent gradient in the biomass and coverage of benthic habitat, which did not appear to be related to bottom stress, but may have been associated with sediment substrate properties. Given the importance of bottom stress in shaping benthic habitats in many other locations around Australia (Pitcher et al. 2002; Pitcher et al. 2004a; Pitcher et al. 2004b and Phillip England, CSIRO Marine and Atmospheric Research, pers. comm.) it is surprising that the analyses showed it to be a non-significant physical factor in determining proportional coverage on the North West Shelf (NWS). During the model development phase of the study a dynamic age-structured metapopulation model was created. This habitat model includes depth and substrate dependent recruitment, growth natural mortality and removal rates by fishing and cyclones. The parameters used in this model were either taken from literature or estimated by minimising the sum of squares between the observed and estimated proportional coverage. The model results easily reproduced the observed patterns of strongly depth related recruitment. It also showed that trawl fishing effort (both by Taiwanese and domestic fleets) was probably a significant factor in shaping the current distribution of benthic habitats on the NWS. There were issues with the models ability to predict recovery rates that match the empirical data. This is almost undoubtedly the result of poorly spatially resolved historical catch time series and a too coarse model resolution. Recasting future analyses and modelling efforts on finer (or more irregular) grids should go a long way to rectifying these issues. Nevertheless, even as is, the model still performs acceptably, particularly within an MSE framework. The bulk of the data (and subsequent modelling efforts) dealt with epibenthic (mainly sponge) habitats. The same model was also applied (in a more limited extent) to seagrass, macroalgae and mangroves. There was substantially less data available for these groups and the models were parameterised from the literature and expert knowledge.

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Maintenance and Update Frequency: unknown

Statement: Original record compiled for the Western Australian Marine Science Institution (WAMSI), Project 3.8, 2008.

Notes

Credit
E. Fulton

Credit
B. Hatfield

Credit
F. Althaus

Credit
K. Sainsbury