This programme enables better monitoring and prediction of changes in fish population biology, fish stock biomass, and size and age composition.
This programme seeks to ensure fisheries stock monitoring and assessment methodologies are robust, standardised where appropriate, and continually upgraded as new or improved methodologies or technologies become available.
Research to support single-species management on the 96 species under the Quota Management System (QMS) is largely funded through fishing industry levies. The industry plays a key role in the prioritisation and evaluation of the research.
The main focus is to:
- monitor and assess stocks with the highest landings or value
- monitor and assess stocks most at risk of being unsustainably fished
- develop techniques to monitor low value species.
Research directions
Stock structure and population biology
We use a variety of approaches and analyses (e.g., genetics, age and growth characterisation) to better define stocks and to determine important aspects of population biology, for example, stock structure, natural mortality, spawning areas.
Monitoring surveys
The data for these analyses are collected in a variety of ways:
Catch sampling
Important information is obtained by taking samples of fish caught in commercial and recreational fishing activity.
Non-commercial fisheries
NIWA is developing approaches to monitor and assess recreational, freshwater, and customary fisheries.
Ageing
NIWA will be further developing ageing methodologies (and standardised protocols) to validate age estimates based on analysis of body structures such as, vertebrae, spines, shells, and otoliths (a bone in the inner ear).
Population Modelling
NIWA is updating and developing routine stock characterisation and stock assessment models (e.g. size and age frequency distributions, catch-per unit-effort) , using standardised techniques and software for fisheries analyses, assessment, and prediction.
We are also developing methods for a wider ecosystem approach (considering the interrelationship between different species and environments) and to appropriately incorporate sources of uncertainty in stock assessment or ecosystem models.
