Evaluating predictability of catch in a highly mixed trawl fishery using stacked and joint species models — ASN Events
  1. Schedule
  2. Session 4.1 | Stock Assessment & Harvest Strategies
  3. Evaluating predictability of catch in a highly mixed trawl fishery using stacked and joint species models

Evaluating predictability of catch in a highly mixed trawl fishery using stacked and joint species models (110152)

James Smith 1 , Daniel Johnson 1
  1. NSW DPI Fisheries, TAYLORS BEACH, NSW, Australia
Evaluating drivers and the predictability of catch is valuable for the management of mixed fisheries. Drivers can represent or help to identify levers for management, and predictable catch compositions are a key component of simulation tools and dynamic management strategies. But modelling mixed fisheries can be challenging due to the large number of taxa, and analysis typically focuses on a few key species or highly aggregated taxa. Here we present results from stacked and joint species models to explore the drivers and predictability of trawl-level catches in an ocean prawn trawl fishery, in New South Wales, Australia. We also evaluate the value of ‘conditional joint prediction’ as a tool for predicting unobserved discards from observed catches. We found that predictive skill was reasonably high, with most of the 130 taxa examined showing a useful level of predictability. We also found that a random forest hurdle model in a stacked framework was the most accurate modelling approach. Conditional joint prediction was not a useful addition, showing that it’s more important to include key environmental variables than to focus on incorporating co-occurring taxa. These results will aid the development of predictive models to enhance and supplement on-board observer catch records.
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